Electroanatomical mapping

ABSTRACT

This invention relates to the determination and/or representation of physiological information relating to a heart surface.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.13/182,768, filed Jul. 14, 2011, which claims priority to ProvisionalApplication 61/432,404, filed Jan. 13, 2011, both of which are hereinincorporated by reference in their entirety.

TECHNICAL FIELD

This invention relates to the determination and representation ofphysiological information relating to a heart surface such aselectroanatomical mapping and annotation.

BACKGROUND

Use of minimally invasive procedures, such as catheter ablation, totreat a variety of heart conditions, such as supraventricular andventricular arrhythmias, is becoming increasingly more prevalent. Suchprocedures involve the mapping of electrical activity in the heart(e.g., based on cardiac signals), such as at various locations on theendocardium surface (“cardiac mapping”), to identify the site of originof the arrhythmia followed by a targeted ablation of the site. Toperform such cardiac mapping a catheter with one or more electrodes canbe inserted into the patient's heart chamber.

Conventional 3D mapping techniques include contact mapping andnon-contact mapping. In contact mapping techniques one or more cathetersare advanced into the heart. Physiological signals resulting from theelectrical activity of the heart are acquired with one or moreelectrodes located at the catheter distal tip after determining that thetip is in stable and steady contact with the endocardium surface of aparticular heart chamber. Location and electrical activity is usuallymeasured sequentially on a point-by-point basis at about 50 to 200points on the internal surface of the heart to construct anelectro-anatomical depiction of the heart. The generated map may thenserve as the basis for deciding on a therapeutic course of action, forexample, tissue ablation, to alter the propagation of the heart'selectrical activity and to restore normal heart rhythm. On the otherhand, in non-contact-based mapping systems a multiple electrode catheteris percutaneously placed in the heart chamber of interest. Once in thechamber, the catheter is deployed to assume a 3D shape. Using thesignals detected by the non-contact electrodes and information onchamber anatomy and relative electrode location, the system providesphysiological information regarding the endocardium of the heartchamber.

SUMMARY

In some aspects, a method for providing information about a patient'sheart includes measuring signals from one or more electrodes at multiplepositions in the heart cavity in response to electrical activity in thepatient's heart cavity over multiple heart beat cycles. The method alsoincludes generating the electroanatomical representation of a patient'sheart based on the signals measured at the electrodes and informationabout the positions of the electrodes. The method also includesgenerating, by a computer, annotation information for the measuredsignals by applying one or more operators to the measured signals. Themethod also includes conveying at least some of the annotationinformation to a user.

Embodiments can include one or more of the following.

The annotation information can include information related to activationtime.

The annotation information can include information related to doubleactivation.

The annotation information can include information related tofractionation.

The annotation information can include information related to voltageamplitude.

The annotation information can include information related to spectralcontent.

Generating the annotation can include identifying at least one ofregions of the heart having double deflections, regions of the hearthaving multiple deflections, regions of the heart having fractionation,and regions of the heart having no activation.

Generating the annotation information can include applying an algorithmto the measured signals to detect double deflections and conveying atleast some of the annotation information can include displaying anindicator on an electroanatomical representation of a patient's heartidentifying regions of double deflections.

Generating the annotation information can include applying an algorithmto the measured signals to detect fractionation and conveying at leastsome of the annotation information can include displaying an indicatoron an electroanatomical representation of a patient's heart identifyingregions of fractionation.

Generating the annotation information can include applying an algorithmto the measured signals to detect no activation and conveying at leastsome of the annotation information can include displaying an indicatoron an electroanatomical representation of a patient's heart identifyingregions of no activation.

The method can also include receiving from an operator a change to theautomatically generated annotation information for a specified measuredsignal and modifying, by the computer, annotation information for one ormore additional measured signals based on the change.

Modifying the annotation information can include automatically modifyingthe annotation information by the computer. Adjusting the annotationinformation for one or more additional measured signals can includeautomatically adjusting annotation information for one or moreadditional measured signals in spatial proximity to the specifiedmeasured signal.

The measured signals in spatial proximity to the specified measuredsignal can include signals at positions within a set distance to thespecified measured signal.

The signals measured at the one or more electrodes can includeelectrograms.

The method can also include receiving from an operator a change to theautomatically generated annotation information for a specifiedelectrogram and automatically, by the computer, adjusting the annotationinformation for other electrograms based on the operator change to theannotation information for the specified electrogram.

The method can also include receiving from an operator a change to anactivation time for a specified measured signal and automatically, bythe computer, adjusting activation times for one or more additionalmeasured signals based on the operator change.

At least some of the signals measured at the one or more electrodes caninclude electrograms.

Generating the annotation information can include generating annotationinformation based on a specified electrogram and spatially neighboringelectrograms.

Spatially neighboring electrograms can include electrograms within apredefined distance.

Generating the annotation information can include for electrogramsincluding multiple deflections, selecting a deflection of the multipledeflections based on timing information for the multiple deflections andtiming information for deflections in spatially neighboring electrogramsand using the selected deflection to determine the annotationinformation.

Spatially neighboring electrograms can include electrograms within a setdistance to the specified measured signal.

Generating the annotation information can include generating annotationinformation for a specific location of the endocardium based on thesignals measured at an electrode location corresponding to the specificlocation and signals measured at one or more additional electrodes atlocations in spatial proximity to the electrode location correspondingto the specific location.

The one or more additional electrodes at locations in spatial proximityto the specific location can include one or more additional electrodeswithin a set distance to the specified measured signal.

Generating the annotation information can include using spatialinformation about the positions at which the signals were measured todetermine local timing information.

Generating the annotation information can include using signals measuredby multiple, different electrodes to determine local timing information.

The one or more electrodes can include one or more electrodes on anintracardiac catheter.

Conveying at least some of the annotation information to the user caninclude displaying an electroanatomical representation of a patient'sheart and at least some of the annotation information.

The method can also include inserting a catheter comprising the one ormore electrodes into the heart cavity and moving the catheter to each ofmultiple, different positions in the heart cavity.

The method can also include synchronizing the signals measured at themultiple positions with one another according to a heart beat cycle.

The method can also include generating the electroanatomicalrepresentation of the patient's heart by determining physiologicalinformation at multiple locations of the endocardium surface based onthe measured signals at the multiple positions by processing thesynchronized signals.

Processing the synchronized signals can include processing thesynchronized signals as though they were obtained at one time.

The method can also include generating an electroanatomicalrepresentation of the patient's heart.

The method can also include generating the electroanatomicalrepresentation of the patient's heart comprises determiningphysiological information by processing the measured signals based atleast in part on a mathematical operator approximating Laplace'sequation.

The method can also include displaying at least a portion of theelectroanatomical representation of a patient's heart.

The method can also include using the electroanatomical representationof a patient's heart to guide treatment of the heart cavity.

The treatment can include ablation of one or more selected regions ofthe heart.

The treatment can include cell therapy, gene therapy, or the applicationof other biological agents.

Generating the electroanatomical representation of the patient's heartcan include determining physiological information at multiple locationsof the endocardium surface by applying a transformation function to thesignals, wherein the transformation function relates signals measuredfrom at least some of the different positions in the heart cavity to thephysiological information at the multiple locations of the endocardiumsurface.

The determination of the physiological information at the multiplelocations of the endocardium surface further can include determining thetransformation function by calculating a forward transformation forrelating the physiological information at the multiple locations of theendocardium surface to the signals measured for the different positionsof the catheter in the heart cavity and inverting the forwardtransformation.

The inverting can include reformulating an underdetermined matrixinversion by regularization.

The inverting can include a least squares minimization.

The method can also include selecting a subset of less than all of thesignals and generating an electroanatomical representation of thepatient's heart based on the selected subset of less than all of thesignals.

Generating the electroanatomical representation of the patient's heartcan include generating the electroanatomical representation of thepatient's heart based on the signals measured at the electrodes andinformation about the positions of the electrodes with respect to theendocardium surface.

In some aspects, a system for providing information about patient'sheart includes one or more electrodes for measuring signals at multiplepositions in the heart cavity in response to electrical activity in thepatient's heart cavity over multiple heart beat cycles. The system alsoincludes an electronic processor coupled to the one or more electrodes.The electronic processor is configured to generate an electroanatomicalrepresentation of the patient's heart based on the signals measured atthe electrodes and information about the positions of the electrodes,generate annotation information for the measured signals by applying oneor more operators to the measured signals, and convey at least some ofthe annotation information to a user.

Embodiments can include one or more of the following.

The annotation information can include information related to activationtime.

The annotation information can include information related to doubleactivation.

The annotation information can include information related tofractionation.

The annotation information can include information related to voltageamplitude.

The annotation information can include information related to spectralcontent.

The electronic processor can configured to generate the annotationinformation by identifying at least one of regions of the heart havingdouble deflections, regions of the heart having multiple deflections,regions of the heart having fractionation, and regions of the hearthaving no activation.

The electronic processor can be configured to generate the annotationinformation by applying an algorithm to the measured signals to detectdouble deflections and the electronic processor can be configured toconvey at least some of the annotation information by displaying anindicator on an electroanatomical representation of a patient's heartidentifying regions of double deflections.

The electronic processor can be configured to generate the annotationinformation by applying an algorithm to the measured signals to detectfractionation and the electronic processor can be configured to conveyat least some of the annotation information by displaying an indicatoron an electroanatomical representation of a patient's heart identifyingregions of fractionation.

The electronic processor can be configured to generate the annotationinformation by applying an algorithm to the measured signals to detectno activation and the electronic processor can be configured to conveyat least some of the annotation information by displaying an indicatoron an electroanatomical representation of a patient's heart identifyingregions of no activation.

The electronic processor can be further configured to receive from anoperator a change to the automatically generated annotation informationfor a specified measured signal and modify annotation information forone or more additional measured signals based on the change.

The electronic processor can be configured to modify the annotationinformation by automatically modifying the annotation information.

The electronic processor can be configured to adjust the annotationinformation for one or more additional measured signals by automaticallyadjusting annotation information for one or more additional measuredsignals in spatial proximity to the specified measured signal.

The measured signals in spatial proximity to the specified measuredsignal can include signals at positions within a radius of influence tothe specified measured signal.

The signals measured at the one or more electrodes can be electrograms.

The electronic processor can be further configured to receive from anoperator a change to the automatically generated annotation informationfor a specified electrogram and automatically adjust the annotationinformation for other electrograms based on the operator change to theannotation information for the specified electrogram.

The electronic processor can be further configured to receive from anoperator a change to an activation time for a specified measured signaland automatically adjust activation times for one or more additionalmeasured signals based on the operator change.

At least some of the signals measured at the one or more electrodescomprise electrograms.

The electronic processor can be configured to generate the annotationinformation by generating annotation information based on a specifiedelectrogram and spatially neighboring electrograms.

Spatially neighboring electrograms can be electrograms within a setdistance to the specified measured signal.

The electronic processor can be configured to generate for electrogramsincluding multiple deflections, selecting a deflection of the multipledeflections based on timing information for the multiple deflections andtiming information for deflections in spatially neighboring electrogramsand using the selected deflection to determine the annotationinformation.

Spatially neighboring electrograms can include electrograms within a setdistance to the specified measured signal.

The electronic processor can be configured to generate the annotationinformation by generating annotation information for a specific locationof the endocardium based on the signals measured at an electrodelocation corresponding to the specific location and signals measured atone or more additional electrodes at locations in spatial proximity tothe electrode location corresponding to the specific location.

The one or more additional electrodes at locations in spatial proximityto the specific location can include one or more additional electrodeswithin a set distance to the specified measured signal.

The electronic processor can be configured to generate the annotationinformation by using spatial information about the positions at whichthe signals were measured to determine local timing information.

The electronic processor can be configured to generate the annotationinformation using signals measured by multiple, different electrodes todetermine local timing information.

The one or more electrodes can be one or more electrodes on anintracardiac catheter.

The electronic processor is further configured to comprising synchronizethe signals measured at the multiple positions with one anotheraccording to a heart beat cycle.

The electronic processor can be configured to generate anelectroanatomical representation of the patient's heart by determiningphysiological information at multiple locations of the endocardiumsurface based on the measured signals at the multiple positions byprocessing the synchronized signals.

The electronic processor can be configured to generate theelectroanatomical representation of the patient's heart by determiningphysiological information by processing the measured signals based atleast in part on a mathematical operator approximating Laplace'sequation.

The electronic processor can be further configured to display at least aportion of the electroanatomical representation of a patient's heart.

The electronic processor can be configured to generate theelectroanatomical representation of the patient's heart by determiningphysiological information at multiple locations of the endocardiumsurface by applying a transformation function to the signals, whereinthe transformation function relates signals measured from at least someof the different positions in the heart cavity to the physiologicalinformation at the multiple locations of the endocardium surface.

The determination of the physiological information at the multiplelocations of the endocardium surface can include determining thetransformation function by calculating a forward transformation forrelating the physiological information at the multiple locations of theendocardium surface to the signals measured for the different positionsof the catheter in the heart cavity and inverting the forwardtransformation.

The electronic processor can be further configured to select a subset ofless than all of the signals and generating an electroanatomicalrepresentation of the patient's heart based on the selected subset ofless than all of the signals.

In some aspects, a method for providing information about a patient'sheart can include measuring signals from one or more electrodes atmultiple positions in the heart cavity in response to electricalactivity in the patient's heart cavity over multiple heart beat cycles.The method can also include generating, by a computer, annotationinformation for the measured signals by applying one or more operatorsto the measured signals to identify at least one of regions of the hearthaving double deflections, regions of the heart having multipledeflections, regions of the heart having fractionation, regions of theheart having double activation, and regions of the heart having noactivation. The method can also include generating, by the computer, anelectroanatomical representation of the patient's heart that includes atleast some of the annotation information.

Embodiments can include one or more of the following.

The annotation information can include information related to activationtime.

The annotation information can include information related to doubleactivation.

The annotation information can include information related tofractionation.

The annotation information can include information related to voltageamplitude.

The annotation information can include information related to spectralcontent.

Generating the annotation information can include applying an algorithmto the measured signals to detect double deflections and generating theelectroanatomical representation can include identifying regions ofdouble deflections.

Generating the annotation information can include applying an algorithmto the measured signals to detect fractionation and generating theelectroanatomical representation can include identifying regions offractionation.

Generating the annotation information can include applying an algorithmto the measured signals to detect no activation and generating theelectroanatomical representation can include identifying regions of noactivation.

The method can also include receiving from an operator a change to theannotation information for a specified measured signal and modifying, bythe computer, annotation information for one or more additional measuredsignals based on the change.

Modifying the annotation information for one or more additional measuredsignals can include automatically adjusting annotation information forone or more additional measured signals in spatial proximity to thespecified measured signal.

Generating the annotation information can include generating annotationinformation based on a specified electrogram and spatially or temporallyneighboring electro grams.

Generating the annotation information can include generating annotationinformation for a specific location of the endocardium based on thesignals measured at an electrode location corresponding to the specificlocation and signals measured at one or more additional electrodes atlocations in spatial proximity to the electrode location correspondingto the specific location.

Generating the annotation information can include generating annotationinformation for a specific location of the endocardium based on thesignals measured at an electrode location corresponding to the specificbeat and signals measured at one or more previous beats at the sameelectrode.

The one or more electrodes can include one or more electrodes on anintracardiac catheter.

The method can also include inserting a catheter comprising the one ormore electrodes into the heart cavity and moving the catheter to each ofmultiple, different positions in the heart cavity.

The method can also include synchronizing the signals measured at themultiple positions with one another according to a heart beat cycle.

The method can also include using the electroanatomical representationof a patient's heart to guide treatment of the heart cavity.

The treatment can include ablation of one or more selected regions ofthe heart.

The treatment can include cell therapy, gene therapy, or the applicationof other biological agents.

The method can also include selecting a subset of less than all of thesignals and generating an electroanatomical representation of thepatient's heart based on the selected subset of less than all of thesignals.

In some aspects, a method for providing information about a patient'sheart can include measuring signals from one or more electrodes atmultiple positions in the heart cavity in response to electricalactivity in the patient's heart cavity over multiple heart beat cycles.The method can also include generating, by a computer, annotationinformation for the measured signals by applying one or more operatorson a specified measured signal and spatially or temporally neighboringmeasured signals. The method can also include generating, by thecomputer, an electroanatomical representation of the patient's heartthat includes at least some of the annotation information.

Embodiments can include one or more of the following.

The annotation information can include information related to activationtime.

The annotation information can include information related to doubleactivation.

The annotation information can include information related tofractionation.

The annotation information can include information related to voltageamplitude.

The annotation information can include information related to spectralcontent.

Generating the annotation information can include applying one or moreoperators to the measured signals to identify at least one of regions ofthe heart having double deflections, regions of the heart havingmultiple deflections, regions of the heart having fractionation, regionsof the heart having double activation, and regions of the heart havingno activation.

The method can also include receiving from an operator a change to theannotation information for a specified measured signal and modifying, bythe computer, annotation information for one or more additional measuredsignals based on the change.

Modifying the annotation information can include automatically modifyingthe annotation information by the computer.

Modifying the annotation information for one or more additional measuredsignals can include automatically adjusting annotation information forone or more additional measured signals in spatial proximity to thespecified measured signal.

The measured signals in spatial proximity to the specified measuredsignal can be signals at positions within a set distance to thespecified measured signal.

The signals measured at the one or more electrodes can be electrograms.

The method can also include receiving from an operator a change to theannotation information for a specified electrogram and automatically, bythe computer, adjusting the annotation information for otherelectrograms based on the operator change to the annotation informationfor the specified electrogram.

Spatially neighboring electrograms can include electrograms within apredefined distance.

Generating the annotation information can include for electrogramsincluding multiple deflections, selecting a deflection of the multipledeflections based on timing information for the multiple deflections andtiming information for deflections in spatially neighboring electrogramsand using the selected deflection to determine the annotationinformation.

Generating the annotation information can include generating annotationinformation for a specific location of the endocardium based on thesignals measured at an electrode location corresponding to the specificlocation and signals measured at one or more additional electrodes atlocations in spatial proximity to the electrode location correspondingto the specific location.

The one or more additional electrodes at locations in spatial proximityto the specific location can include one or more additional electrodeswithin a predefined distance to the specific location.

Generating the annotation information can include generating annotationinformation for a specific location of the endocardium based on thesignals measured at an electrode location corresponding to the specificbeat and signals measured at one or more previous beats at the sameelectrode.

Generating the annotation information can include using spatialinformation about the positions at which the signals were measured todetermine local timing information.

The one or more electrodes can be one or more electrodes on anintracardiac catheter.

The method can also include using the electro anatomical representationof a patient's heart to guide treatment of the heart cavity.

The method can also include selecting a subset of less than all of thesignals and generating an electroanatomical representation of thepatient's heart based on the selected subset of less than all of thesignals.

In some aspects system for providing information about patient's heartincludes one or more electrodes for measuring signals at multiplepositions in the heart cavity in response to electrical activity in thepatient's heart cavity over multiple heart beat cycles. The system alsoincludes an electronic processor coupled to the one or more electrodesconfigured to generate annotation information for the measured signalsby applying one or more operators to the measured signals to identify atleast one of regions of the heart having double deflections, regions ofthe heart having multiple deflections, regions of the heart havingfractionation, regions of the heart having double activation, andregions of the heart having no activation and generate by the computer,an electroanatomical representation of the patient's heart that includesat least some of the annotation information.

Embodiments can include one or more of the following.

The electronic processor can be configured to generate the annotationinformation by applying an algorithm to the measured signals to detectdouble deflections and the electronic processor can be configured togenerate the electroanatomical representation by identifying regions ofdouble deflections.

The electronic processor can be configured to generate the annotationinformation by applying an algorithm to the measured signals to detectfractionation and the electronic processor can be configured to generatethe electroanatomical representation by identifying regions offractionation.

The electronic processor can be configured to generate the annotationinformation by applying an algorithm to the measured signals to detectno activation and the electronic processor can be configured to generatethe electroanatomical representation by identifying regions of noactivation.

The electronic processor can be further configured to receive from anoperator a change to the automatically generated annotation informationfor a specified measured signal and modify annotation information forone or more additional measured signals based on the change.

The electronic processor can be configured to adjust the annotationinformation for one or more additional measured signals by automaticallyadjusting annotation information for one or more additional measuredsignals in spatial proximity to the specified measured signal.

The measured signals in spatial proximity to the specified measuredsignal can be signals at positions within a set distance to thespecified measured signal.

The electronic processor can be further configured to receive from anoperator a change to an activation time for a specified measured signaland automatically adjust activation times for one or more additionalmeasured signals based on the operator change.

At least some of the signals measured at the one or more electrodes canbe electrograms.

The electronic processor can be configured to generate the annotationinformation by generating annotation information based on a specifiedelectrogram and spatially or temporally neighboring electrograms.

Spatially neighboring electrograms can be electrograms within a setdistance to the specified measured signal.

The electronic processor can be configured to generate the annotationinformation by for electrograms including multiple deflections,selecting a deflection of the multiple deflections based on timinginformation for the multiple deflections and timing information fordeflections in spatially neighboring electrograms and using the selecteddeflection to determine the annotation information.

Spatially neighboring electrograms comprise electrograms within a setdistance to the specified measured signal.

The electronic processor can be configured to generate the annotationinformation by generating annotation information for a specific locationof the endocardium based on the signals measured at an electrodelocation corresponding to the specific location and signals measured atone or more additional electrodes at locations in spatial proximity tothe electrode location corresponding to the specific location.

The electronic processor can be configured to generate the annotationinformation by generating annotation information for a specific locationof the endocardium based on the signals measured at an electrodelocation corresponding to the specific beat and signals measured at oneor more previous beats at the same electrode.

The one or more electrodes can be one or more electrodes on anintracardiac catheter.

In some aspects, a system for providing information about patient'sheart can include one or more electrodes for measuring signals atmultiple positions in the heart cavity in response to electricalactivity in the patient's heart cavity over multiple heart beat cycles.The system can also include an electronic processor coupled to the oneor more electrodes, wherein the electronic processor is configured togenerate annotation information for the measured signals by applying oneor more operators a specified measured signal and spatially ortemporally neighboring measured signals and generate anelectroanatomical representation of the patient's heart that includes atleast some of the annotation information.

Embodiments can include one or more of the following.

The electronic processor can be further configured to generating theannotation information by applying one or more operators to the measuredsignals to identify at least one of regions of the heart having doubledeflections, regions of the heart having multiple deflections, regionsof the heart having fractionation, regions of the heart having doubleactivation, and regions of the heart having no activation.

The electronic processor can be further configured to receive from anoperator a change to the annotation information for a specified measuredsignal and modify annotation information for one or more additionalmeasured signals based on the change.

The electronic processor can be further configured to adjust theannotation information for one or more additional measured signals byautomatically adjusting annotation information for one or moreadditional measured signals in spatial proximity to the specifiedmeasured signal.

The measured signals in spatial proximity to the specified measuredsignal can be signals at positions within a set distance to thespecified measured signal.

The signals measured at the one or more electrodes can be electrograms.

The electronic processor can be further configured to receive from anoperator a change to the annotation information for a specifiedelectrogram and automatically adjust the annotation information forother electrograms based on the operator change to the annotationinformation for the specified electrogram.

Spatially neighboring electrograms can be electrograms within apredefined distance.

The electronic processor can be further configured to generate theannotation information by for electrograms including multipledeflections, selecting a deflection of the multiple deflections based ontiming information for the multiple deflections and timing informationfor deflections in spatially neighboring electrograms and using theselected deflection to determine the annotation information.

The electronic processor can be further configured to generate theannotation information by generating annotation information for aspecific location of the endocardium based on the signals measured at anelectrode location corresponding to the specific location and signalsmeasured at one or more additional electrodes at locations in spatialproximity to the electrode location corresponding to the specificlocation.

The one or more additional electrodes at locations in spatial proximityto the specific location can be one or more additional electrodes withina predefined distance to the specific location.

The electronic processor can be further configured to generate theannotation information by generating annotation information for aspecific location of the endocardium based on the signals measured at anelectrode location corresponding to the specific beat and signalsmeasured at one or more previous beats at the same electrode.

The electronic processor can be further configured to generate theannotation information using spatial information about the positions atwhich the signals were measured to determine local timing information.

The one or more electrodes can be one or more electrodes on anintracardiac catheter.

In some aspects, a method for providing information about a patient'sheart can include measuring signals from one or more electrodes atmultiple positions in the heart cavity in response to electricalactivity in the patient's heart cavity over multiple heart beat cycles,generating, by a computer, annotation information for the measuredsignals by applying one or more operators to the measured signals, andreceiving from an operator a change to the annotation information for aspecified measured signal, modifying, by the computer, annotationinformation for one or more additional measured signals based on thechange, and generating, by the computer, an electroanatomicalrepresentation of the patient's heart that includes at least some of theannotation information and at least some of the modified annotationinformation.

Embodiments can include one or more of the following.

Generating the annotation information can include identifying at leastone of regions of the heart having double deflections, regions of theheart having multiple deflections, regions of the heart havingfractionation, regions of the heart having double activation, andregions of the heart having no activation.

Modifying the annotation information for one or more additional measuredsignals can include automatically adjusting annotation information forone or more additional measured signals in spatial proximity to thespecified measured signal.

Modifying the annotation information for one or more additional measuredsignals can include automatically adjusting annotation information forone or more additional measured signals in temporal proximity to thespecified measured signal.

Generating the annotation information can include generating annotationinformation based on a specified electrogram and spatially or temporallyneighboring electrograms.

The one or more electrodes can be one or more electrodes on anintracardiac catheter.

The method can also include using the electroanatomical representationof a patient's heart to guide treatment of the heart cavity.

In some aspects, a method for providing information about anelectroanatomical representation of a patient's heart, the methodincludes measuring signals at one or more electrodes at multiplepositions in the patient's heart cavity over a time period includingmultiple heart beat cycles, at least some of the signals being inresponse to electrical activity in the patient's heart cavity. Themethod also includes applying an algorithm to one or more specificsignals of the measured signals to determine a triggering event in thespecific signal. The method also includes synchronizing, by thecomputer, the signals measured at the one or more electrodes with oneanother according to a heart beat cycle based on the triggering eventand generating, by the computer, the electroanatomical representation ofthe patient's heart based on the synchronized measured signals andpositions of the catheter electrodes.

Embodiments can include one or more of the following.

Applying the algorithm to the specific signal to determine thetriggering event can include selecting portions of the specific signalto process to determine the triggering event based on a second,different signal of the measured signals.

Selecting portions of the specific signal can include selecting portionsof the specific signal to exclude from processing.

Selecting portions of the specific signal can include selecting portionsof the specific signal to include in processing.

The method can also include processing the second signal to determine anevent corresponding in time to a potential false triggering event in thespecific signal and selecting portions of the specific signal to processto determine the triggering event can include selecting a portion of thespecific signal that excludes the time period including the potentialfalse triggering event.

Applying the algorithm to the one or more specific signals signal todetermine the triggering event can include processing the specificsignal using a sliding window integration to generate a reference signaland analyzing the reference signal to determine the triggering event.

Applying the algorithm to the one or more specific signals to determinethe triggering event can include processing the specific signal togenerate a representation of instantaneous energy and analyzing therepresentation of instantaneous energy to determine the triggeringevent.

Applying the algorithm to the one or more specific signals to determinethe triggering event can include applying an algorithm to generate arepresentation of the signal having reduced jitter and analyzing therepresentation of the signal having reduced jitter to determine thetriggering event.

Applying the algorithm to the one or more specific signals to determinethe triggering event can include applying an algorithm to integrate thesignal over a window and apply an operator to ensure the result of thealgorithm is positive.

The one or more electrodes can include one or more electrodes on anintracardiac catheter.

The method can also include generating, by the computer, annotationinformation for the measured signals by applying one or more algorithmsto the measured signals.

The method can also include conveying at least some of the annotationinformation to the user.

The method can also include inserting a catheter comprising the one ormore electrodes into the heart cavity and moving the catheter to each ofmultiple, different positions in the heart cavity.

Generating the electroanatomical representation of the patient's heartcan include determining physiological information at multiple locationsof the endocardium surface based on the measured signals at the multiplepositions by processing the synchronized signals.

Processing the synchronized signals can include processing thesynchronized signals as though they were obtained at one time.

Generating the electroanatomical representation of the patient's heartcan include determining physiological information by processing themeasured signals based at least in part on a mathematical operatorapproximating Laplace's equation.

The method can also include displaying at least a portion of theelectroanatomical representation of a patient's heart.

The method can also include using the electroanatomical representationof a patient's heart to guide treatment of the heart cavity.

The treatment can include ablation of one or more selected regions ofthe heart.

The treatment can include cell therapy, gene therapy, or the applicationof other biological agents.

Generating the electroanatomical representation of the patient's heartcan include determining physiological information at multiple locationsof the endocardium surface by applying a transformation function to thesignals, wherein the transformation function relates signals measuredfrom at least some of the different positions in the heart cavity to thephysiological information at the multiple locations of the endocardiumsurface.

The determination of the physiological information at the multiplelocations of the endocardium surface can include determining thetransformation function by calculating a forward transformation forrelating the physiological information at the multiple locations of theendocardium surface to the signals measured for the different positionsof the catheter in the heart cavity and inverting the forwardtransformation.

The inverting can include reformulating an underdetermined matrixinversion by regularization.

The inverting further can include a least squares minimization.

The method can also include selecting a subset of less than all of thesignals. Generating the electroanatomical representation of thepatient's heart can include generating the electroanatomicalrepresentation of the patient's heart based on the selected subset ofless than all of the signals.

In some aspects, a system for providing information about anelectroanatomical representation of a patient's heart includes one ormore electrodes for measuring signals at multiple positions in thepatient's heart cavity over a time period including multiple heart beatcycles, at least some of the signals being in response to electricalactivity in the patient's heart cavity. The system also includes anelectronic processor coupled to the one or more electrodes, wherein theelectronic processor is configured to apply an algorithm to one or morespecific signals of the measured signals to determine a triggering eventin the specific signal, synchronize the signals measured at the one ormore electrodes with one another according to a heart beat cycle basedon the triggering event, and generate the electroanatomicalrepresentation of the patient's heart based on the synchronized measuredsignals and positions of the catheter electrodes.

The electronic processor can be configured to apply the algorithm to thespecific signal to determine the triggering event by selecting portionsof the specific signal to process to determine the triggering eventbased on a second, different signal of the measured signals.

The electronic processor can be configured to select portions of thespecific signal by selecting portions of the specific signal to excludefrom processing.

The electronic processor can be configured to select portions of thespecific signal by selecting portions of the specific signal to includein processing.

The electronic processor can be further configured to process the secondsignal to determine an event corresponding in time to a potential falsetriggering event in the specific signal and select portions of thespecific signal to process to determine the triggering event byselecting a portion of the specific signal that excludes the time periodincluding the potential false triggering event.

The electronic processor can be configured to apply the algorithm to theone or more specific signals signal to determine the triggering event byprocessing the specific signal using a sliding window integration togenerate a reference signal and analyzing the reference signal todetermine the triggering event.

The electronic processor can be configured to apply the algorithm to theone or more specific signals to determine the triggering event byprocessing the specific signal to generate a representation ofinstantaneous energy and analyzing the representation of instantaneousenergy to determine the triggering event.

The electronic processor can be configured to apply the algorithm to theone or more specific signals to determine the triggering event byapplying an algorithm to generate a representation of the signal havingreduced jitter and analyzing the representation of the signal havingreduced jitter to determine the triggering event.

The electronic processor can be configured to apply the algorithm to theone or more specific signals to determine the triggering event byapplying an algorithm to integrate the signal over a window and apply anoperator to ensure the result of the algorithm is positive.

The one or more electrodes can be one or more electrodes on anintracardiac catheter.

The electronic processor is further configured to comprising synchronizethe signals measured at the multiple positions with one anotheraccording to a heart beat cycle.

The electronic processor can be configured to generate anelectroanatomical representation of the patient's heart by determiningphysiological information at multiple locations of the endocardiumsurface based on the measured signals at the multiple positions byprocessing the synchronized signals.

The electronic processor can be configured to generate theelectroanatomical representation of the patient's heart by determiningphysiological information by processing the measured signals based atleast in part on a mathematical operator approximating Laplace'sequation.

The electronic processor can be further configured to display at least aportion of the electroanatomical representation of a patient's heart.

The electronic processor can be configured to generate theelectroanatomical representation of the patient's heart by determiningphysiological information at multiple locations of the endocardiumsurface by applying a transformation function to the signals, whereinthe transformation function relates signals measured from at least someof the different positions in the heart cavity to the physiologicalinformation at the multiple locations of the endocardium surface.

The determination of the physiological information at the multiplelocations of the endocardium surface can include determining thetransformation function by calculating a forward transformation forrelating the physiological information at the multiple locations of theendocardium surface to the signals measured for the different positionsof the catheter in the heart cavity and inverting the forwardtransformation.

The electronic processor can be further configured to select a subset ofless than all of the signals and generating an electroanatomicalrepresentation of the patient's heart based on the selected subset ofless than all of the signals.

In some aspects, a method for providing information about anelectroanatomical representation of a patient's heart includes measuringsignals at one or more electrodes at multiple positions in the patient'sheart cavity over a time period including multiple heart beat cycles, atleast some of the signals being in response to electrical activity inthe patient's heart. The method also includes processing, by a computer,the measured signals to determine a metric for each of the multipleheart beat cycles. The method also includes selecting, by the computer,a subset of the measured signals based on the metric associated with theheart beat cycle. The method also includes generating, by the computer,the electroanatomical representation of the patient's heart based on theselected subset of measured signals and positions of the electrodes.

Embodiments can include one or more of the following.

The metric can be a beat metric.

Measuring signals at the one or more electrodes can include measuring asignal indicative of tissue proximity.

The signal indicative of tissue proximity can be a force measurement.

The signal indicative of tissue proximity can be an impedancemeasurement.

Processing the signals to determine a the metric can include processingthe signals to determine a measure of tissue proximity and selecting thesubset of the signals based on the metric can include selecting thesubset of signals based on the measure of tissue proximity.

Selecting the subset of signals indicative of the measure of tissueproximity can include selecting signals within about 3 mm of theendocardium surface.

Measuring signals at the one or more electrodes can include measuring aforce on a catheter.

Measuring the signals can include measuring a signal indicative ofcontact with the endocardium surface, processing the signals todetermine the metric can include processing the signal indicative ofcontact with the endocardium surface to determine a force measure, andselecting the subset of the signals based on the metric can includeselecting the subset of signals having a force measure within apredetermined range.

Selecting the subset of signals having a force measure within apredetermined range can include selecting signals having a force measureabove a first threshold and below a second threshold.

The metric can include an indication of contact with the endocardiumsurface.

The metric can include an indication of signal propagation andselecting, by the computer, the subset of the measured signals based onthe metric can include selecting the subset of signals having metricsassociated with normal signal propagation.

Measuring the signals can include measuring a first signal at a firstelectrode located in a first stable location and measuring a secondsignal at a second electrode located in a second stable location that isspaced apart from the first stable location, processing the measuredsignals can include determining a timing difference between activationsin the first signal and the second signal, and selecting the subset ofthe measured signals can include selecting the subset of signals havinga timing difference within a predetermined range.

Selecting the subset of the measured signals based on the metric caninclude selecting a subset of the measured signals for beats duringwhich capture of a pacing signal occurred.

Measuring the signals can include measuring a pacing signal andmeasuring a second signal located in a stable location, and processingmeasured signals can include determining a timing difference between thepacing signal and activation in the second signal, the timing differenceproviding information associated with capture of the pacing signal bythe patient's heart.

The metric can include an indication of ventrical activation andselecting, by the computer, the subset of the measured signals based onthe metric can include selecting the subset of signals having metricsassociated with the absence of ventricular activation.

The beat metric can include an indication of a far field signal andselecting, by the computer, the subset of the measured signals based onthe metric can include selecting the subset of signals having metricsassociated with the absence of the far field signal.

The metric can include an indication of electrogram consistency forspatially correlated signals, with the spatially correlated signalsbeing measured at similar locations within the heart cavity, andselecting, by the computer, the subset of the measured signals based onthe metric can include selecting the subset of signals having anelectrogram consistency that is within a predetermined range.

Processing signals to determine a metric can include processing locationinformation associated with the signals to determine signals measured atsimilar locations and processing the signals measured at similarlocations to determine a measure of similarity between the signalsmeasured at the similar locations and selecting, by the computer, thesubset of the measured signals can include selecting the subset ofsignals based on the measure of similarity.

The metric can include an indication of electrogram consistency fortemporally related signals, and selecting, by the computer, the subsetof the measured signals based on the metric can include selecting thesubset of signals having an electrogram consistency that is within apredetermined range.

Processing the signals to determine the metric can include processingthe signals to determine a measure of similarity between at least twospatially correlated signals and selecting the subset of signalscomprises selecting a subset of signals based on the determined measureof similarity between the at least two spatially correlated signals.

Processing the signals to determine the metric can include processingthe signals to determine a measure of similarity between at least twotemporally correlated signals and selecting the subset of signalscomprises selecting a subset of signals based on the determined measureof similarity between the at least two temporally correlated signals.

The metric can be an indication of rapid changes in an electrogram.

Measuring the signals at one or more electrodes can include measuringthe signals at one or more intracardiac electrodes in response toelectrical activity in the patient's heart cavity.

The method can also include displaying the electroanatomicalrepresentation of the patient's heart.

The method can also include inserting a catheter comprising the one ormore electrodes into the heart cavity and moving the catheter to each ofmultiple, different positions in the heart cavity.

The method can also include synchronizing the signals measured at themultiple positions with one another according to a heart beat cycle.

Generating the electroanatomical representation of the patient's heartcan include determining physiological information at multiple locationsof the endocardium surface based on the measured signals at the multiplepositions by processing the synchronized signals.

Processing the synchronized signals can include processing thesynchronized signals as though they were obtained at one time.

Generating the electroanatomical representation of the patient's heartcan include determining physiological information by processing themeasured signals based at least in part on a mathematical operatorapproximating Laplace's equation.

The method can also include displaying at least a portion of theelectroanatomical representation of a patient's heart.

The method can also include using the electroanatomical representationof a patient's heart to guide treatment of the heart cavity.

The treatment can include ablation of one or more selected regions ofthe heart.

The treatment can include cell therapy, gene therapy, or the applicationof other biological agents.

Generating the electroanatomical representation of the patient's heartcan include determining physiological information at multiple locationsof the endocardium surface by applying a transformation function to thesignals, wherein the transformation function relates signals measuredfrom at least some of the different positions in the heart cavity to thephysiological information at the multiple locations of the endocardiumsurface.

The determination of the physiological information at the multiplelocations of the endocardium surface further can include determining thetransformation function by calculating a forward transformation forrelating the physiological information at the multiple locations of theendocardium surface to the signals measured for the different positionsof the catheter in the heart cavity and inverting the forwardtransformation.

The inverting can include reformulating an underdetermined matrixinversion by regularization.

The inverting can include a least squares minimization.

Generating the electroanatomical representation of the patient's heartcan include generating the electroanatomical representation based on theselected subset of measured signals and positions of the electrodes withrespect to the endocardium surface.

In some aspects, a system for providing information about anelectroanatomical representation of a patient's heart includes one ormore electrodes for measuring signals at multiple positions in thepatient's heart cavity over a time period including multiple heart beatcycles, at least some of the signals being in response to electricalactivity in the patient's heart. The system also includes an electronicprocessor coupled to the one or more electrodes. The electronicprocessor is configured to process the measured signals to determine ametric for each of the multiple heart beat cycles, select a subset ofthe measured signals based on the metric associated with the heart beatcycle, and generate the electroanatomical representation of thepatient's heart based on the selected subset of measured signals andpositions of the electrodes.

Embodiments can include one or more of the following.

The metric comprises a beat metric.

At least one of the one or more electrodes can be configured to measurea signal indicative of tissue proximity.

The signal indicative of tissue proximity can be a force measurement.

The signal indicative of tissue proximity can be an impedancemeasurement.

The electronic processor can be further configured to process thesignals to determine a the metric by processing the signals to determinea measure of tissue proximity and select the subset of the signals basedon the metric by selecting the subset of signals based on the measure oftissue proximity.

The electronic processor can be further configured to select the subsetof signals indicative of the measure of tissue proximity by selectingsignals within about 3 mm of the endocardium surface.

At least one of the one or more electrodes can be configured to measurea force on a catheter.

At least one of the one or more electrodes can be configured to measurea signal indicative of contact with the endocardium surface and theelectronic processor can be further configured to process the signals todetermine the metric by processing the signal indicative of contact withthe endocardium surface to determine a force measure and select thesubset of the signals based on the metric by selecting the subset ofsignals having a force measure within a predetermined range.

The electronic processor can be further configured to select the subsetof signals having a force measure within a predetermined range byselecting signals having a force measure above a first threshold andbelow a second threshold.

The metric can be an indication of contact with the endocardium surface.

The metric can be an indication of signal propagation and the electronicprocessor can be further configured to select the subset of the measuredsignals based on the metric by selecting the subset of signals havingmetrics associated with a desired signal propagation.

The one or more electrodes can include a first electrode located in afirst stable location configured to measure a first signal and a secondelectrode located in a second stable location that is spaced apart fromthe first stable location configured to measure a second signal and theelectronic processor can be further configured to process the measuredsignals by determining a timing difference between activations in thefirst signal and the second signal and select the subset of the measuredsignals by selecting the subset of signals having a timing differencewithin a predetermined range.

The electronic processor can be further configured to select the subsetof the measured signals based on the metric by selecting a subset of themeasured signals for beats during which capture of a pacing signaloccurred.

The one or more electrodes can include electrodes configured to measurea pacing signal and a second signal located in a stable location and theelectronic processor can be further configured to process measuredsignals by determining a timing difference between the pacing signal andan activation in the second signal, the timing difference providinginformation associated with capture of the pacing signal by thepatient's heart.

The metric can be an indication of ventrical activation and theelectronic processor can be further configured to select the subset ofthe measured signals based on the metric by selecting the subset ofsignals having metrics associated with the absence of ventricularactivation.

The beat metric can be an indication of a far field signal and theelectronic processor can be further configured to select the subset ofthe measured signals based on the metric by selecting the subset ofsignals having metrics associated with the absence of the far fieldsignal.

The metric can be an indication of electrogram consistency for spatiallycorrelated signals, with the spatially correlated signals being measuredat similar locations within the heart cavity and the electronicprocessor can be further configured to select the subset of the measuredsignals based on the metric by selecting the subset of signals having anelectrogram consistency that is within a predetermined range.

The electronic processor can be further configured to select process thesignals to determine the metric by processing location informationassociated with the signals to determine signals measured at similarlocations and processing the signals measured at similar locations todetermine a measure of similarity between the signals measured at thesimilar locations and select the subset of the measured signalscomprises selecting the subset of signals based on the measure ofsimilarity.

The metric can be an indication of electrogram consistency fortemporally related signals and the electronic processor is furtherconfigured to select the subset of the measured signals based on themetric by selecting the subset of signals having an electrogramconsistency that is within a predetermined range.

The electronic processor can be further configured to process thesignals to determine the metric by processing the signals to determine ameasure of similarity between at least two spatially correlated signalsand selecting the subset of signals comprises selecting a subset ofsignals based on the determined measure of similarity between the atleast two spatially correlated signals.

The electronic processor can be further configured to process thesignals to determine the metric by processing the signals to determine ameasure of similarity between at least two temporally correlated signalsand selecting the subset of signals comprises selecting a subset ofsignals based on the determined measure of similarity between the atleast two temporally correlated signals.

The metric can be an indication of rapid changes in an electrogram.

The one or more electrodes can be one or more electrodes on anintracardiac catheter.

The electronic processor is further configured to comprising synchronizethe signals measured at the multiple positions with one anotheraccording to a heart beat cycle.

The electronic processor can be configured to generate anelectroanatomical representation of the patient's heart by determiningphysiological information at multiple locations of the endocardiumsurface based on the measured signals at the multiple positions byprocessing the synchronized signals.

The electronic processor can be configured to generate theelectroanatomical representation of the patient's heart by determiningphysiological information by processing the measured signals based atleast in part on a mathematical operator approximating Laplace'sequation.

The electronic processor can be further configured to display at least aportion of the electroanatomical representation of a patient's heart.

The electronic processor can be configured to generate theelectroanatomical representation of the patient's heart by determiningphysiological information at multiple locations of the endocardiumsurface by applying a transformation function to the signals, whereinthe transformation function relates signals measured from at least someof the different positions in the heart cavity to the physiologicalinformation at the multiple locations of the endocardium surface.

The determination of the physiological information at the multiplelocations of the endocardium surface can include determining thetransformation function by calculating a forward transformation forrelating the physiological information at the multiple locations of theendocardium surface to the signals measured for the different positionsof the catheter in the heart cavity and inverting the forwardtransformation.

The electronic processor can be further configured to select a subset ofless than all of the signals and generating an electroanatomicalrepresentation of the patient's heart based on the selected subset ofless than all of the signals.

It is believed that the systems and methods described herein, canprovide quick and automatic ways to aggregate data acquired overmultiple cardiac cycles while keeping the data synchronized andselecting only data that can be used to generate a reliableelectroanatomical map.

It is also believed that the systems and methods described herein, canprovide quick and automatic ways to generate annotation information anddisplay the annotation information with the electroanatomical map.

Embodiments of the system may also include devices, software,components, and/or systems to perform any features described above inconnection with the methods described herein.

Embodiments of the methods and systems generally disclosed herein can beapplied to determining the position of any object within an organ in apatient's body such as the patient's heart, lungs, brain, or liver.

As used herein, the “position” of an object means information about oneor more of the 6 degrees of freedom that completely define the locationand orientation of a three-dimensional object in a three-dimensionalcoordinate system. For example, the position of the object can include:three independent values indicative of the coordinates of a point of theobject in a Cartesian coordinate system and three independent valuesindicative of the angles for the orientation of the object about each ofthe Cartesian axes or any subset of such values.

As used herein, “heart cavity” means the heart and surrounding tissue.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. In case of conflict withdocuments incorporated herein by reference, the present documentcontrols.

The details of one or more embodiments of the invention are set forth inthe accompa-nying drawings and the description below. Other features,objects, and advantages of the invention will be apparent from thedescription and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart of an exemplary electro-anatomical mappingprocess.

FIG. 2 shows exemplary waveforms with applied blanking windows.

FIG. 3 shows exemplary data signals from and ECG and an electrode in thecoronary sinus.

FIG. 4 shows an exemplary data signals and processed data signals.

FIG. 5 show exemplary data signals measured on an electrode in theCoronary Sinus.

FIG. 6 shows an exemplary data signals for respiration detection.

FIG. 7 shows an exemplary data signals for propagation time detection.

FIG. 8 shows an exemplary data signals for far field overlap detection.

FIGS. 9 and 10 show exemplary data signals for annotationdeterminations.

FIG. 11 shows exemplary electro-anatomical maps.

FIG. 12 shows exemplary data signals for annotation determinations.

FIG. 13 shows exemplary data signals for annotation determinations andadjustments.

FIG. 14 shows a schematic diagram of an exemplary system.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

Systems and methods are disclosed herein that provide a way toautomatically integrate measurements taken over multiple heart beatsinto a single cardiac map while selecting and keeping only heart beatsthat can be used to generate a reliable electroanatomical map. Systemsand methods are also disclosed herein that provide a way toautomatically generate annotation information and display the annotationinformation with the electroanatomical map.

Systems and methods for automating the process of generating anelectroanatomical map are disclosed herein. Electroanatomical maps canbe used to guide the catheter ablation treatment of cardiac arrhythmiaby providing information on the anatomy and cardiac excitation to helppinpoint the source of the arrhythmia. Existing mapping methodologiesrely on numerous manual operator inputs limiting mapping speed,repeatability and resolution.

Thousands of electrogram measurements are necessary in order to map agiven cardiac chamber with sufficient accuracy and resolution.Automation during data acquisition and map construction enables acomputer to process the large amount of data in a timely and accuratemanner. Systems and methods for automating the generation of reliableelectroanatomical maps using a computer are described herein.

In general, cardiac mapping systems can be used for automaticallygenerating different types of maps (e.g., with limited humanintervention). Such maps display electrical data, anatomical data, or acombination of both, and aid physicians in determining the source ofarrhythmias and in guiding therapeutic treatment, often in the form ofRF ablation. An exemplary mapping system is described, for example, inU.S. Pat. No. 7,515,954, entitled “NON-CONTACT CARDIAC MAPPING,INCLUDING MOVING CATHETER AND MULTI-BEAT INTEGRATION” and filed Jun. 13,2006, the contents of which is incorporated by reference herein in itsentirety.

Some non-automated (e.g., manual) mapping methodologies can involveoperator review and input for each set of incoming data. In order toallow adequate time for review of the incoming data, a limited set ofinformation is provided to the operator for interpretation in order todecide which data to add to the mapping dataset. While manual mappingallows the generation of maps, the mapping process is relatively timeconsuming and the quality of the maps highly operator dependent. Cardiaccycle length typically ranges from 0.15-1.5 s, a rate too fast for anoperator to manually review all incoming data during mapping. As aresult, with manual mapping most collected data is ignored therebyslowing the mapping process and limiting map accuracy. In addition,manual mapping relies on quick analysis performed by the operator whichcan lead to inconsistencies due to varying level of operator skill andoperator error. Systems and methods for automating the process ofgenerating and possible annotating an electroanatomical map aredisclosed herein.

The impact of the application of the system and method described hereinon mapping time and map resolution can be high. For example, based onreported literature values, in manual point by point mapping systems,the rate of point acquisition is 3 points per minute. Following atypical mapping effort lasting 30 minutes, a map containing roughly 90data points on the cardiac surface is obtained. Using a multi-electrodemapping catheter such as described in U.S. patent application Ser. No.12/005,975 entitled “CARDIAC MAPPING CATHETER” and filed on Dec. 18,2007, the contents of which is incorporated by reference herein in itsentirety, in 14 human patients has led to an average of 500 points perminute, with typical mapping times of 10 minutes providing 5,000 datapoints on the cardiac surface.

FIG. 1 shows a flow chart of an exemplary automated electro-anatomicalmapping process. A data stream (102) containing multiple signals isfirst input into the system. The datastream may include signals such asintracardiac electrograms, surface electrocardiograms (ECG), electrodelocation information originating from a variety of methodologies(magnetic, impedance, ultrasound, real time MRI, etc.), tissue proximityinformation, catheter force information (force spring sensing,piezo-electric sensing, optical sensing etc.), catheter electricalcoupling information, respiration phase and other physiologicalinformation, etc. For the generation of specific types of maps, one ormore signals may then be used as reference to trigger and align the datastream relative to the cardiac, other biological cycle or anasynchronous system clock resulting in beat datasets (104).

For each incoming beat dataset a number of beat metrics are computed(108). Beat metrics may be computed using information from a singlesignal, spanning multiple signals within the same beat or from signalsspanning multiple beats. The beat metrics provide multiple types ofinformation on the quality of the specific beat dataset or likelihoodthat the beat data is good for inclusion in the map dataset. Once beatmetrics are computed, a beat acceptance process (106) aggregates thecriteria and decides which beat datasets will make up the map dataset(108).

A surface map generation process (120) is then employed to generatesurface map data from the map dataset and surface geometry data. Surfacegeometry data may be generated concurrently during the same dataacquisition process using identical or different triggering and beatacceptance metrics employing a surface geometry construction process(112). This process constructs surface geometry using data such aselectrode locations and catheter shape contained in the data stream.Alternatively, previously collected surface geometry may be used as aninput to surface map data (116). Such geometry may have been collectedpreviously in the same procedure using a different map dataset, or usinga different modality such as CT, MRI, ultrasound, rotationalangiography, etc. and registered to the catheter locating system. Thesystem selects the source of the surface geometry data (114) andprovides surface geometry data (118) to the surface map generationprocess (120). The generation process (120) generates surface map data(122). The surface map data (122) may provide information on cardiacelectrical excitation, cardiac motion, tissue proximity information,tissue impedance information, force information, or any other collectedinformation desirable to the clinician. Once obtained, the surface mapdata may be further processed to annotate desired features from theunderlying data, a process defined as surface map annotation (124).Desired annotations can include instantaneous potential, activationtime, voltage amplitude, dominant frequency and other properties of thesignal. Once computed, the annotations are displayed superimposed onchamber geometry. If the number of annotations is lower than the numberof elements that make up the display of surface geometry, surface mapinterpolation may be employed (126). Displayed maps can be computed anddisplayed separately, or overlaid on top of each other (128).

Datastream

Referring back to FIG. 1, during the automated electro-anatomicalmapping process a data stream (102) provides a collection ofphysiological and non-physiological signals that serve as inputs to themapping process. The signals may be collected directly by the mappingsystem, or obtained from another system using an analog or digitalinterface.

The data stream may include signals such as unipolar or bipolarintracardiac electrograms (EGM), surface electrocardiograms (ECG),electrode and/or catheter location information originating from avariety of methodologies (magnetic, impedance, ultrasound, fluoroscopy,real time MRI, etc.), tissue proximity information, catheter force/orcontact information obtained from a variety of methodologies, cathetertip or tissue temperature, acoustic information, catheter electricalcoupling information, respiration phase, blood pressure and otherphysiological information. In addition, the dataset may containadditional information such as catheter shape, electrode properties,etc.

Triggering

Referring back to FIG. 1, during the automated electro-anatomicalmapping process a triggering process (104) defines a time instancearound which a window of data from the data stream is sampled. In somecases, a trigger event is detected from a physiological signaldesignated as a reference signal. In other cases the trigger isasynchronous to the patient and derived from a system clock. Forexample, when constructing an activation map it is common to use an ECGor EGM signal as a reference. When constructing an anatomical shell,however, such reference may not be necessary and system clock canprovide a trigger.

When aggregating data from multiple cardiac beats to create anelectroanatomical map, it is be useful to trigger off of a stablereference in the data stream. The reference provides alignment acrossbeats to a desired phase in the cardiac cycle. In some examples, asingle signal source is selected for triggering (e.g. ECG lead II) andwaveform attributes such as minimum/maximum, absolute maximum,maximum/minimum slope, or first deviation from baseline are used todetect a trigger. Signal morphology attributes, catheter motion andnoise sources can make it challenging to reliably and consistentlytrigger with such simplified schemes. Inaccurate triggering, in turn,may lead to corruption in the map dataset and resultantelectroanatomical map. It is believed that using multiple signals todetermine triggering can provide various advantages in comparison totriggering schemes based on a single signal.

Blanking

In some embodiments, it may be impractical to consistently trigger on agiven signal using the signal's waveform alone using simple criteria.For example, when mapping in the right or left atrium it is oftendesirable to use a bipolar intracardiac signal as reference. To avoidtiming inaccuracy, it is important for this signal to trigger on atrialrather than ventricular activation. A bipolar electrode pair positionedin the Coronary Sinus (“CS”) is frequently used as a reference for thispurpose. Nonetheless, depending on patient specific anatomy, the bipolarelectrode pair may measure atrial and ventricular activation withcomparable amplitude. As a result, when using a single signal source, itis difficult to consistently trigger on the desired atrial activation.Existing solutions to this problem include searching for a different setof electrodes and when those are not available, repositioning thecatheter with the hope of finding a better trigger site. It isfrequently the case that neither approach is successful. This inventionprovides a means to overcome this problem using an additional signalwith dominant ventricular activation as a blanking reference.

FIG. 2 provides an example of this approach. Waveform B is a schematicof the waveform used for reference triggering. The waveform has twodominant activations 130 and 132, the lower amplitude activation 132being the desirable reference trigger. Clinically, these two activations130 and 132 can be ventricular and atrial activations measured on abipolar Coronary Sinus electrode pair, the atrial activation 132 beingthe desirable trigger. In order to reliably trigger on the loweramplitude atrial signal 32, an additional waveform is employed, waveformA. In this waveform, the undesirable activation is dominant and easy todetect (e.g., as shown in activations 134). Clinically, this may be leadII of a surface ECG signal where the trigger is the R wave. Thiswaveform can be defined as the blanking reference waveform. The timingdetection algorithm can employ any of maximum, minimum, maximum orminimum derivative, deviation from baseline, etc. as a detectioncriteria to find the blanking reference timing of interest. Once theblanking reference is detected (e.g., once the timing of activation 134is detected), a blanking window 136 is defined for waveform B. Theblanking window 136 has a defined offset and duration relative to theblanking reference. The offset and duration are determined to be largeenough so they include the entire undesirable activation duration, butnot too large so as to include the desirable activation timing. Typicalvalues when using ECG II as blanking reference waveform and CoronarySinus as reference trigger are 40 ms for offset and 120 ms for duration.When determining the trigger in waveform B, signal is ignored during theblanking window. In this manner, the impact of the undesirable signal iseffectively ignored.

It is important to note that this approach can be used with otherscenarios and signals. For example, cardiac stimulation is oftenemployed during mapping. It may be desirable to trigger off of abiological signal rather than the stimulation signal. In a manneridentical to the one described above, a waveform with large stimulationsignal may be employed as a blanking reference waveform. A waveform withboth stimulation artifact and signal indicative of biological activationmay then be used as the reference trigger.

Furthermore, there may be situations where more than one blankingreference is used to determine triggering (e.g., two blanking signals,three blanking signals, four blanking signals). For example, it ispossible that both stimulator artifact and ventricular activation arepresent on a signal where the desired trigger is atrial. In this casetwo blanking references can be defined simultaneously.

In addition, rather than a blanking window, the blanking reference candefine an inclusion window. In this case the reference trigger inwaveform B may be determined only during the inclusion windowconstructed around the blanking reference in waveform A. For example,this may be desirable when mapping in the ventricle. Once again,waveform A may be ECG lead II and waveform B a bipolar set of electrodesin the Coronary Sinus. In this case, an inclusion window around theblanking reference will be used to find ventricular activation duringthe inclusion window in the Coronary Sinus waveform.

FIG. 3 shows blanking applied on signal collected from a human patient.Waveform A is the blanking reference waveform, in this case ECG lead V4.Waveform B is the CS waveform. A window 140 around the R wave 140 isapplied on the CS signal. When looking for a reference trigger oractivation time on this signal, the period during which the blankingwindow 140 exists is ignored.

Blanking is useful in triggering set-up but can also be used forannotation. For example, blanking can be applied to individual EGMs,using a common blanking waveform reference to avoid far field effects.Voltage amplitude and other annotations can also be derived from thebenefit from blanking.

Powered Triggering

FIG. 4 waveform A provides an example of a bipolar Coronary Sinus signalacquired in a human patient. As the waveform demonstrates, the bipolarsignal may exhibit multiple upstroke components 150 and downstrokecomponents 152 during local activation. Furthermore, the morphology ofthe signal may change substantially with small changes in activation andcatheter movement. As shown with arrows in waveform A, the timing of thelocal maximum and minimum changes frequently and would lead to timingjitter in the map dataset. For example, in the activation 154 the firstupstroke component would provide the local maximum while in activation156 the second upstroke component would provide the local maximum.Similarly, in the activation 154 the first downstroke component wouldprovide the local minimum while in activation 156 the second downstrokecomponent would provide the local minimum. Thus, timing based on thelocal maximum or local minimum would experience timing jitter due to theselection of different activation times within the signal.

As before, existing solutions to this problem can include searching fora different set of electrodes and when those are not available,repositioning the catheter with the hope of finding a more stabletrigger site. Systems and methods described herein can overcome thisproblem applying additional processing to the signal to search for apeak in instantaneous power or an equivalent measure. Such triggeringwill be called powered triggering.

For a given sampled signal, S(n), the equation below provides thepowered triggering operator:

$\begin{matrix}{{{S_{p}(n)} = \sqrt{\sum\limits_{i = {- N}}^{N}\;\left\lbrack {a_{i} \cdot {S\left( {n + i} \right)}} \right\rbrack^{2}}}{a_{i} = \begin{Bmatrix}{{0\mspace{14mu}{if}\mspace{14mu}{i}} > N} \\{{\frac{1}{{2 \cdot N} + 1}\mspace{14mu}{if}\mspace{14mu}{i}} \leq N}\end{Bmatrix}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

This operator squares and sums the signal over a finite window of 2·N+1samples. Once applied, simple local maximum detection can be robustlyapplied as shown in FIG. 4 waveform B. Maximum derivative detection canalso be applied in this case. In practice, a window duration of 70 mswas found to perform well.

This operation is an instantaneous power estimate and as such is signinvariant and less susceptible to local morphology changes. It should beappreciated that variations on this operator could be provided. Thoseinclude applying an absolute value operator or any even power to thesignal. In addition, more elaborate window function such as Hanning,Kaiser, Blackman etc. or a frequency selective window could be employed.

This operator is useful in triggering set-up but can also be used forannotation. For example, the operator can be applied to individual EGMs,and the activation timing found as the maximum. Voltage amplitude andother annotations can also be derived from the powered signal.

Beat Metrics and Acceptance

Referring back to FIG. 1, following triggering on a desired signal (104)beat metrics (108) are used to determine beat acceptance (106). Eachtrigger event is referred to as a beat, even though the event may betriggered from non-cardiac information. In the beat metric andacceptance process, a beat window is defined around the occurrence of atrigger event. While triggering identifies a desired event in areference signal, many additional factors affect the quality andrelevance of data collected in any given beat. For example attributessuch as cardiac cycle length, catheter velocity, respiration phase,patient movement, injury current etc. can affect the relevance of thedata for inclusion in a map dataset for certain types ofelectroanatomical maps.

In order to create a map dataset, some elements of a beat acceptancescheme is automatically applied by a computer to incoming beats. Thescheme applies a predetermined set of beat metrics in order to add intothe map dataset only beats that meet certain criteria. In the case of acatheter with multiple electrodes a beat metric can affect the inclusionor exclusion of all information collected by the catheter during theduration of the beat. Alternatively, the beat metric can apply to anddictate the inclusion or exclusion of a subset of the informationcollected by the catheter. In manual mapping systems this activity isperformed by the operator with limited information guiding theirdecision. Furthermore, beats arrive at a rate ranging 0.2-1.5 seconds, arate too fast for operator review.

Beat metrics can be directly used by an electroanatomical mapping systemto automate the mapping process. Nonetheless, the information providedby the beat metrics can also be presented and used as additionaloperator input in a manual mapping system.

Beat metrics may be designed to provide both a binary YES/NO acceptancedecision as well as a value indicative of the acceptance level. Whenaggregating different beat metrics into a beat acceptance decision alogical AND can be applied to all beat metrics. If desired, a moreelaborate function can be applied by the computer to either the set ofbinary decisions or values in order to determine if a beat is to beadded into the map dataset.

Beat acceptance and beat metrics may run in real time as the data streamis arriving (e.g., the computer can process the data stream as thedatastream is received). In addition, the scheme can be rerun followingdata collection, potentially with different parameters (e.g., thecomputer can re-process the data after collection). For example, apatient may present with two intermittent rhythms during mapping. Duringthe mapping process a map dataset containing only beats with one rhythmmay be collected, the other rhythm being rejected. Following the mappingprocess, beat metrics can be recomputed and acceptance applied to acceptthe second rhythm and produce a second map. Alternatively, the tworhythms can be mapped simultaneously by setting up two beat acceptanceand mapping pipelines, each designed to accept and reject a differentdesired rhythm.

The following describes different exemplary beat metrics that can beapplied to incoming beats. It should be understood that other types ofbeat metrics can also be applied.

Mechanically Based Metrics

Respiration

Patient respiration has a number of effects on the heart mapping system.It leads to a movement of the heart within the chest which can affectthe accuracy of the catheter or electrode locating system. Respirationalso causes a deformation of the heart shape which can be as high as 7mm in certain parts of the anatomy (“A Study of the Motion andDeformation of the Heart Due to Respiration,” Kate McLeish, IEEETransactions On Medical Imaging, VOL. 21, NO. 9, September 2002). Inaddition, respiration can displace diagnostic catheters modifying theirEGM measurement and may even slightly modulate cardiac rhythm.

As a result it is desirable to form a respiration beat metric used todetect respiration phase and collect data during a consistent period inthe respiratory cycle. The beat metric can employ a number of schemes tocollect respiration phase data.

In cases where a patient is mechanically ventilated, respiration phaseinformation can be provided directly through an interface to theventilator. Respiration can also be detected using a variety of standardmeans such as monitor belt and acoustic sensing.

Particularly applicable respiration phase sensing schemes during acatheter ablation procedure include impedance detection and locationsensor. In impedance detection, current injecting electrodes can beplaced on the body surface or in the body and set to inject current in adesired frequency. The same current injecting electrodes, or any otherelectrodes, can be used to monitor to the resultant potential which willhave a significant respiratory effect in it. FIG. 5 depicts the waveformmeasured on a body surface electrode positioned on the chest wheninjecting current between electrodes in the Coronary Sinus and a bodysurface electrode. As the figure shows, the signal is periodic. A rangeof values on the waveform can define the desired range and be used forthresholding as shown by the bold dotted lines 170, 172 in the figure.The beat metric value is the difference between the average value of thewaveform during the beat window and the center of the desired range(e.g., as indicated by the location where the signal crosses the dottedline 174). Alternatively, the beat metric value can be the differencebetween the median during the window or the instantaneous value duringthe timing of the reference trigger and the center of the desired range.When the beat metric value is within the desired range the beat metricreceives a YES decision.

Alternatively, a location sensor can also be placed on the chest or inthe body. The location sensor may, for example, use magnetic locatingtechnology. Sensor location can be plotted over time with a similarrange thresholding scheme described above. FIG. 6 shows respirationdetection using location information in a human patient. Waveform Ashows ECG, and waveform B shows the corresponding X, Y and Z coordinatesof a location sensor placed on the chest. In order to reduce the 3coordinates to one waveform used for thresholding, principal componentanalysis is applied to the X, Y and Z coordinates. The first componentis plotted in waveform C and used for thresholding. Multiple locationsensors on or in the patient can also be used in a similar manner.

Tissue Proximity

An important problem in the construction of electroanatomical maps istissue proximity determination. When a mapping catheter is maneuvered inthe heart, it is difficult to determine if it is in contact or inproximity to cardiac tissue. Various methods including EGM review,ultrasound, flourscopy and tactile feel can be employed to determinecontact. However, those may lack proper sensitivity and specificity andmay be difficult to incorporate in an automated mapping procedure. Forexample, when the catheter is in contact with infracted tissue, EGMproperties and mechanical catheter movement may be indistinguishablefrom those when the catheter is not in contact. Knowledge of tissueproximity is valuable for both the constriction of the anatomical andelectrical map.

A variety of methods can be employed to determine tissue proximity. Forexample, tissue proximity assessment using impedance information such asdescribed in U.S. patent application Ser. No. 12/437,794 or coupling forexample as described in U.S. patent application Ser. No. 12/096,071, thecontents of each of which are hereby incorporated by reference in theirentirety can also be used as inputs for this beat metric. In the case ofimpedance information, a current injecting electrode on the mappingcatheter injects a current. Measurements collected by the injectingelectrode and/or other potential measuring electrodes can be used todetermine information about cardiac tissue and its proximity to thecatheter and its electrodes. This information can subsequently beprovided on a per electrode basis, or globally for the entire catheter.

A surface geometry construction algorithm may require electrodepositions with close wall proximity as an input. Tissue proximityinformation can be used in this case with threshold values such that thecomputer system makes a determination about whether to accept a beatbased on the tissue proximity information and only accept beats and/orelectrode locations whose tissue proximity values indicate smalldistance to the wall. For example, a threshold of 3 mm may be used.

Similarly, for electrical mapping, only beats and/or electrode locationswhere tissue proximity information indicates proper wall distance aredesired in the map dataset. Range thresholding can be similarly appliedby the computer system to generate a beat metric and decision to includeonly those measurements in the map.

Contact force between the catheter and the wall can also be determinedand used as metric. Force can be measured using a number of technologiesincluding piezoelectric crystals for example as described in U.S. patentapplication Ser. No. 11/553,965, location information on sensorsseparated by a resilient deformable member at the catheter tip forexample as described in U.S. patent application Ser. No. 11/868,733, andoptical sensing for example as described in U.S. patent application Ser.No. 11/237,053.

Force values can be divided into 3 ranges. The first range is low force,e.g. F<8 g, which indicates no wall contact. The second range is anintermediate amount of force, e.g. 8 g<F<40 g, which indicates properwall contact. The third range is high force, e.g. F>40 g, whichindicates excessive force possibly tenting and deforming cardiacanatomy.

Surface geometry construction algorithms used to create chamber anatomymay require a map dataset with catheter positions both inside thechamber and with proper wall contact. In this case, force informationcan be directly used as a beat metric with a range thresholding schemedesigned to only accept beats whose force is under the excessive forcethreshold. For surface geometry construction algorithms that requirepositions with proper wall contact alone as an input, force informationcan be used with threshold values that only accept beats whose forcevalue indicates proper wall contact.

For electrical mapping, only beats where the catheter force informationindicates proper wall contact are desired in the map dataset. Rangethresholding can be similarly applied by the computer system to generatea beat metric and decision to include only those measurements in themap.

Catheter Movement

The mapping catheter is moved by the clinician to different sites inorder to collect measurements in multiple locations. In addition, thecatheter experiences motion due to cardiac contraction. The mappingsystem may assign a single location to electrodes during the beat windowusing averaging, median or gating to the reference trigger or windowcenter. Excessive catheter motion during the beat window may lead toinaccuracy in the location used to generate the map.

A beat metric can be defined to use the catheter velocity as an input.Using a range thresholding scheme described above excessive cathetermotion can automatically rejected from the map dataset (e.g.,automatically rejected by the computer system without substantial humaninteraction).

Patient Movement

Similarly to respiration, patient movement can affect cardiac anatomyand catheter tracking accuracy. A beat metric can be used to detectpatient motion and reject data during and/or following patient movement.

In the case of an external field generator, a single or multiplelocation sensors can be place on the body surface or in the body in astable location providing patient location reference. The baselineposition of the patient location reference can then be recorded. Oncemapping begins, the distance between the baseline position and currentposition of the patient location reference can be generated using arange thresholding scheme. The patient location reference andthresholding can be completed automatically by a computer system basedon an algorithm that does not require substantial human input.

Electrogram/Electrocardiogram Based Metrics

Various factors can affect the consistency of the electrical propagationsequence in the heart during mapping. A few examples include,intermittent rhythm, pacing failing to capture, and catheter physicalcontact leading to premature contractions. It is therefore critical toverify that the underlying rhythm is the one desired for mapping beforeadding beats to the map. Different metrics can be used to accomplishthis using a computer system. The computer system can receive datainputs, analyze the data, and make a determination about whether toinclude collected data in an electroanatomical map and/or whether toprovide an annotation based on the observed consistency of theelectrical propagation sequence in the heart.

Cycle Length

The period of time between reference triggers is defined as cyclelength. Cardiac cycle length typically ranges 0.2-1.5 s. During anunstable rhythm, cycle length is likely to vary across beats.Conversely, during a stable rhythm, cycle length is expected to staystable within a certain tolerance.

Cycle length can be computed by the computer for every incoming beat.Because a computer system (as opposed to a human operator) analyzes theincoming signals to compute cycle length, the determinations can be madein real time without delaying collection of the data. One of twoschemes, absolute and relative, can be used as in a range thresholdingscheme to derive a beat metric from it. In an absolute scheme, a cyclelength value is defined and the computer compares all incoming beats tothat cycle length value. The value can be measured while the desiredrhythm is taking place. In other examples, the computer system canexecute a relative scheme by comparing the current cycle length to theone of the preceding beat, or some other weighted function ofneighboring beats. Based on the cycle length information, the computersystem can determine whether to include the data from a particular cyclein the electroanatomical map.

Propagation Reference

While cycle length is a powerful metric for detecting rhythm, it is aglobal measure that samples a single electrogram. As such, cycle lengthdoes not verify the propagation sequence in the heart chamber and mayallow beats with different underlying rhythms to be included in theelectroanatomical map. The computer system can use a propagationreference to provide additional validation that the desired propagationsequence is taking place by measuring the relative timing between asecond cardiac signal and the reference trigger. The propagationreference uses the same triggering scheme as the reference trigger, andcan use the same criteria (e.g., minimum/maximum) and enhancements(e.g., blanking) when triggering. For example, in one case the referencetrigger can be a bipolar signal from the CS while propagation referencecomes from bipolar electrodes in the right atrial appendage. Once thetiming of both signals is detected, their relative timing can be used asa beat metric. In another example pacing may be used. In this case thereference trigger may be the stimulator signal while the propagationreference can be a biological signal. Similar to the previous example,if pacing is taking place in the CS, a bipolar signal from the rightatrial appendage can be used as the propagation reference. This case canprovide particular advantages because the propagation reference may beused to verify pacing capture. When pacing, cycle length alone may betotally dependent on the stimulator input and as such provide no usefulphysiological information for mapping.

FIG. 7 provides a schematic example of the above. Waveform A is theblanking waveform used to provide a blanking trigger as previouslydescribed. Waveform B is the reference trigger and waveform C is thepropagation reference. The relative timing between the triggers inwaveforms B and C is used as a baseline and deviation from the baselineis used as beat metric. In this case the chosen relative timing is 30ms. When the computer system determines the difference in timing betweenthe reference trigger and the propagation reference exceeds a thresholdthe computer system rejects the beat and the beat signals from the beatare not included in the data set used to generate the electroanatomicalmap. For example, looking at the figure, if |T_(C)−T_(B)−30 ms|≦5 ms thebeat is accepted.

Similar to cycle length, either an absolute baseline difference or arelative difference can be used by the computer system for a metric. Inthe case of an absolute difference, an initial timing difference is usedas baseline while the desired rhythm is observed. The computer systemcompares the timing difference of subsequent beats to the baselinevalue. A range thresholding scheme is then applied to derive a value anddecision for the beat metric. The computer system can also calculate arelative difference and use the relative difference in this case suchthat the relative timing of the current beat is compared to that of theprevious one.

It should be understood that more than two EGMs can be compared toverify propagation sequence. A number of propagation references can bedefined each of which provides a baseline value. The beat metric can bean average of the differences.

Furthermore, waveform aspects other than timing alone can be used toverify a consistent relationship between EGMs. For example, amathematical operator such as the one described in Equation 1 can beapplied by the computer system on the propagation reference waveform. Asdescribed, the operator reduces the deflections in the waveform andprovides a measure of local activation timing and energy. Subsequently,correlation or root-mean-squared difference can be applied as themetric.

Far Field Overlap

Electrogram recordings aim to measure an electrical signal emanatingfrom tissue close to the electrode. Nonetheless, electrodes can pick upsignal from tissue that is further away, particularly if the activationin local tissue is substantially smaller in amplitude than that of thesignal that is further away. The signal picked up by the electrode fromtissue that is further away is called far field signal. When annotatingelectrogram recording in an electroanatomical mapping system, it isimportant to annotate properties of local tissue, rather than those ofthe far field component. For example, when collecting recordings incertain areas in the atria (particularly when close to the tricuspid andmitral annulus) ventricular far field signal may dominate therecordings, even when nearby atrial tissue is healthy.

In certain rhythms, particularly fast rhythms, a far field component maybe present on a recording during some beats but not during others.Examples include, atrial and ventricular dissociation, or an n:1 (n>1)relationship between the two. In those cases it is possible to define abeat metric that rejects those beats that overlap with the presence of afar field signal and avoid having the EGMs mis-annotated. The computersystem can analyze incoming signals to determine the presence of a farfield component. For example, an additional data signal can be measuredto detect the presence of the far field signal. If the far field signalis detected, the computer system can use the timing of the detectedsignal to provide a blanking window during which data from the EGM datais discarded. Thus, if a significant far field component is detected;the computer system discards the data (e.g., not include the data fromthe beat in the data set used to generate the electroanatomical map).

FIG. 8 shows ECG and EGM recordings from an atrial tachycardia patientwith 2:1 atrial ventricular conduction (as shown in signals A and B,respectively). Ventricular activation is detected from the ECG signal inwaveform A. The detected signal 180 from the ECG is used by the computersystem both as a blanking reference and as a far field rejectionreference. The blanking reference is used to robustly trigger on atrialactivation in the CS signal in waveform B which is used as a referencetrigger, as discussed previously. A beat window is defined around thereference trigger (e.g., beat windows 184 a, 184 b, 184 c, and 184 d).In this example, the beat window spans is slightly less than the fulltachycardia cycle length (90%). In addition, a far field rejectionwindow 182 a, 182 b is defined by the computer system around the farfield rejection reference 180, in this case spanning 110 ms. The beatmetric value calculated by the computer system is the overlap betweenthe beat window (e.g., beat windows 184 a, 184 b, 184 c, and 184 d) andthe far field rejection window (e.g., windows 182 a, 182 b). Wheneverthe overlap exceeds 0 ms, for example, the metric can return a NOdecision and the beat whose window contains ventricular activation isautomatically rejected by the computer system. In this example, farfield rejection window 182 a overlaps with beat window 184 a and, farfield rejection window 182 b overlaps with beat window 184 s. Due to theoverlap of the far field rejection window with beats 182 a and 182 c,the data from beats 182 a and 182 c is discarded and not used by thecomputer system to generate the electroanatomical map.

EGM Consistency

In some cases it is important to use attributes of electrogramscollected by the mapping catheter as beat metrics. For example, injurycurrent is a local change of activation which can result from mappingcatheter tissue contact. In the case of injury current, EGMs recorded bythe mapping catheter will be altered, while EGMs on other catheters andthe rhythm remain unchanged. EGMs containing injury current cantherefore be mis-annotated by the mapping system. It is thereforebelieved to be valuable to provide a beat metric in which the computersystem automatically monitors the EGMs measured by the mapping catheter.One such beat metric is EGM consistency.

EGM consistency looks to verify that measured EGMs are consistent withina certain period of time or location. One type of EGM consistency metriccan be EGM correlation between the current and previous beat. In thismetric, the computer system correlates the EGM in each electrode on themapping catheter to that of the previous beat (e.g., signals with atemporal relationship are compared to one another). An averagecorrelation across all electrodes is then determined by the computersystem. If the average correlation exceeds a certain value (e.g., 0.7)the beat is accepted. This metric has a good probability of eliminatingrecording corruptions that are intermittent, such as injury currentdiscussed above. In addition, this metric is able to reject beats whenthe catheter moved very quickly since the recorded EGMs are likely tochange as the catheter is moved.

Rather than the previous beat, EGMs can alternatively be compared tothose previously added to the map in a nearby location (e.g., signalswith a spatial relationship are compared to one another can becorrelated by the computer system).

It is important to note that other methods can be applied to determineEGM consistency. For example the average root-mean-square of EGMs acrossall or some of the electrodes in neighboring beats or locations can becomputed by the computer system. In addition, attributes of the EGMs,rather than EGM themselves can be compared for consistency. An exampleof such measure would be the detection of activation time on each EGMand the computation of change in activation time across electrodes inneighboring beats computed.

Is it also important to note that this metric can be applied on a perelectrode, rather than per beat basis.

Surface Geometry Construction

The surface geometry construction algorithm generates the anatomicalsurface on which the electroanatomical map is displayed. Surfacegeometry can be constructed using a system as described U.S. patentapplication Ser. No. 12/437,794 entitled “Impedance Based AnatomyGeneration” and filed on May 8, 2008, the contents of which isincorporated by reference herein in its entirety.

Alternatively, an anatomical shell can be constructed by the computersystem by fitting a surface on electrode locations that are determinedeither by the user or automatically to be on the surface of the chamber.In addition, a surface can be fit on the outermost electrode and/orcatheter locations within the chamber.

As described, the map dataset from which the surface is constructed canemploy identical or different beat acceptance criteria from those usedfor electrical and other types of maps. The map dataset for surfacegeometry construction can be collected concurrently with electrical dataor separately.

Surface geometry can be represented as a mesh containing a collection ofvertices (points) and the connectivity between them (e.g. triangles).Alternatively, surface geometry can be represented by differentfunctions such as higher order meshes, NURBS, or curvilinear shapes.

Surface Map Generation

The combination of map dataset and surface geometry data allows forsurface map generation. The surface map is a collection of values orwaveforms (e.g. electrograms) on the surface of the chamber of interest,whereas the map dataset can contain data that is not on the cardiacsurface. One approach for processing the map dataset and surfacegeometry data to obtain a surface map dataset is described in U.S. Pat.No. 7,515,954, entitled “NON-CONTACT CARDIAC MAPPING, INCLUDING MOVINGCATHETER AND MULTI-BEAT INTEGRATION” and filed Jun. 13, 2006, thecontents of which is incorporated by reference herein in its entirety.

Alternatively or in combination with the method above, an algorithm thatapplies acceptance criteria to individual electrodes can be employed.For example, electrode locations exceeding a set distance (e.g. 3 mm)from surface geometry can be rejected. Another algorithm can incorporatetissue proximity information using impedance for inclusion in thesurface map data. In this case only electrode location whose proximityvalue is <3 mm would be included. Additional metrics of the underlyingdata can also be used for this purpose. For example, EGM propertiessimilar to beat metrics can be assessed on a per electrode basis. Inthis case metrics such as far field overlap, EGM consistency, can beused.

It should be understood that variations on the method to project pointsfrom the map dataset to the surface or to select appropriate points canexist.

Map Annotation

Once data is collected into surface map data, attributes relating to thecollected data may be automatically presented to the user. Theseattributes can be automatically determined and applied to the data bythe computer system and are referred to herein as annotations. Exemplaryannotations include activation time, the presence of double activationor fractionation, voltage amplitude, spectral content, etc. Due to theabundance of data available in automated mapping (e.g., mappingcompleted by the computer system with minimal human input related to theincoming data), it is not practical for the operator to review andannotate data manually. However, human input can be a valuable additionto the data, and so when user input is provided it is necessary for thecomputer system to automatically propagate and apply it to more than onedata point at a time.

It is possible to use the computer system to automatically annotateactivation time, voltage, and other characteristics of individualelectrograms. Activation time detection uses methods similar to thosepreviously described to detect a trigger and can similarly benefit fromthe use of blanking and powered triggering operator.

Spatial Consistency

To further improve accuracy, in some cases it is useful to considerneighboring electrograms during annotation. One form of consideringneighboring electrograms is spatial consistency: the computer systemautomatically adjusts annotations to improve physiological plausibilityand reduce map noise by making the annotations more spatiallyconsistent.

For activation time mapping, three common conditions benefit fromspatial consistency:

Along a line of conduction block, electrograms frequently exhibit two ormore distinct deflections. Due to small variations in electrodeposition, orientation, and motion, natural variation in activationstrength, and electrical noise, the strength of the deflections withinelectrograms will vary between beats even for a catheter held in thesame nominal location. For electrograms with multiple deflections, thisvariation in deflection strength causes automatic timing annotation torandomly switch between the deflections when the strengths of thosedeflections are similar. This results in jagged contours and mottling ofthe activation map along the line of block which is physiologicallyimprobable or impossible. This map noise may hinder understanding of theactivation pattern. In order to reduce the effects of multipledeflections, the computer system automatically compares the activationtiming determined for the multiple beats and modifies the timing toselect a different deflection when differences between temporallycorrelated signals exist.

For macroreentrant circuits, the beat window should be close to thetypical cycle length to show the activation pattern for the entirechamber. Due to normal cycle length variation, some cycles will beslightly shorter than the beat window. In some regions, the electrogramsmeasured during these shorter cycles will have deflections at both thestart and end of the beat window, where the deflection at the end isactually the next beat. Since the deflection strength at the start andend is similar, automatic timing annotation may switch between the startand end of the cycle. This introduces mottling of the activation map inthe region where the activation is transitioning from the end to thestart of the beat window.

In regions of fractionation, electrograms may have long periods ofrelatively weak but similar strength activation. This occurs because theelectrode is measuring activation that wends along a slow and convolutedpath through mostly scar tissue near the electrode. Automatic timingannotation in these regions may appear entirely random because theselected time corresponds to an arbitrary peak in the fractionatedelectrogram, not the nominal activation time at that location. Thisobscures the direction of propagation through these regions.

FIG. 9 and FIG. 10 show 4 waveforms collected from a human patient.Going from top to bottom, the first waveform is ECG lead V6, the secondwaveform is a bipolar recording from CS electrodes 9-10, the thirdwaveform is a unipolar electrotram in the mapping site and the forthwaveform is the time derivative of the unipolar recording. FIG. 9 showsthree beats in one location near a line of block (the first commoncondition). The minimum unipolar electrogram slope, used in this case astiming annotation, alternates between the earlier and later deflectionbetween beats. This occurs despite minimal catheter motion and a stablerhythm.

FIG. 10 shows a beat where both the current and next activation justfall within the beat window (the second common condition). Automatictiming annotation may take the early or late timing depending on whichdeflection happens to be larger.

When manually annotating activation time, experienced operators considerboth the electrogram being annotated and the surrounding activationtimes to create a consistent and physiologically plausible map. Themethod described below performs a similar function automatically.

Spatial Consistency Method

The Spatial Consistency Method provides a way for the computer system toautomatically reduce spatial variation in annotations in a way that isconsistent with the individual electrograms and with an adjustabledegree of variation reduction.

This method has three stages:

(a) Individual electrogram analysis,

(b) Electrogram clustering, and

(c) Annotation adjustment.

Individual Electrogram Analysis

During this step, each electrogram is analyzed by the computer systemusing the annotation criteria previously described to extract annotationcandidates. The annotation criteria define what aspects of the signalare used to determine activation and prescribe a minimum activationthreshold. An annotation candidate is an electrogram sample that exceedsthe annotation threshold and is a local annotation criteria extrema.Electrograms without annotations candidates are considered to have noactivation.

The computer system assigns a confidence value to every annotationcandidate of every electrogram. Many possible mappings from electrogramcharacteristics to confidence values are possible. In one exemplarymethod, the mapping maintains three properties:

(a) Stronger deflections should have higher confidences;

(b) Similar strength deflections should have similar confidences acrossall electrograms; and

(c) The numerical difference between confidence values should correspondto the likelihood that higher confidence value is preferred.

One way to do this mapping is for the computer system to normalize theannotation criteria amplitude to the range of annotation criteriaamplitudes observed across all electrograms at samples that exceed thedetection threshold. This mapping is believed to fulfill the first twoproperties enumerated above and adequately represents the thirdproperty.

If degree of variation reduction is zero, the computer system selectsthe annotation candidate with the highest confidence value for eachelectrogram for the annotation for that electrogram. If the somevariation reduction is enabled, the computer system uses the confidencevalues during the annotation adjustment to reduce spatial variation.

Electrogram Clustering

For each electrogram included in the map, a set of neighboringelectrograms (e.g., spatially neighboring electrograms or electrogramswithin a predetermined distance from one another) and associated weightsare defined. One way of automatically defining these sets by thecomputer system is to include all electrograms whose projected locationis within a specified distance (e.g., a radius of inclusion or radius ofinfluence) of the projected location of each electrogram included in themap. A variety of associated weighting functions could be used. Oneoption is the inverse of the distance; another is the cosine of theratio of the distance to the maximum distance. Both these methods areused in the iterative annotation adjustment as described below.

Annotation Adjustment

Annotation adjustment is a computer implemented optimization procedurethat improves spatial consistency to the target amount by automaticallyidentifying and changing the least certain annotations first. The goalis that annotations for electrograms with multiple weak deflectionsshould migrate from the strongest deflection to the deflection that isclosest to consistent with neighboring annotations as the target degreeof spatial consistency is increased. This produces an increasinglysmooth map while changing the annotations that are most likely to beerroneous.

The optimization problem is implemented by the computer system andinvolves a large number of coupled variables (one variable perelectrogram annotation, which depends on the annotations of allelectrogram's neighbor) and is highly non-linear (each annotation canonly take a discrete set of values, and those values vary significantlyacross the map and between neighbors).

To solve this problem in a tractable duration, a greedy iterativealgorithm can be used. This algorithm includes four steps:

(a) The computer system initializes all annotations with the highestconfidence candidate or marked as no annotation if no candidate exists.

(b) For each annotation, the computer system combines neighboringannotations to produce an estimate of the annotation. This uses aninterpolation function from the neighboring annotations to the currentannotation.

(c) For each annotation, the computer system computes the costassociated with switching from the current annotation to theinterpolated annotation.

(d) Based on the calculated costs, the computer system selects thelowest cost annotation change and replaces that annotation with theinterpolated value. Increment the aggregate cost by the cost associatedwith this adjustment. Update the interpolated annotations forneighboring annotations. Repeat this step while the aggregate cost isless than the target total cost, which is based on the target degree ofspatial consistency.

For the interpolation function, a number of formulations are possible.One formulation is inverse distance weighting with snapping to theclosest candidate. For reentrant maps, the interpolation should evaluatethe base interpolation function twice for early and late points, oncewhile treating all points as early and once as late.

Many cost functions that combine the candidate confidences in variousways may be applied to this problem. One method is to take thearithmetic difference between the confidences.

This iterative algorithm incrementally “smoothes” the annotations bymoving the least certain annotations first but only to acceptableelectrogram features.

FIG. 11 shows an example of applying this method to an activation map.The arrows indicate a number of electrogram sites with doubleactivations that are incorrectly timed before applying the spatialconsistency operation. After applying this operation, the map shows aclearly defined line of block that is physiologically plausible.

Automatic Categorical Annotation

Certain electrogram categories are of particular clinical significancewhen constructing an electroanatomical map. Since the operator is unableto manually review each point in the surface map data, it is importantto automatically annotate those categories by the computer system. Theseinclude electrograms with double deflections, multiple deflections,fractionation, and/or no activation.

Automatically annotating electrograms with these categories assists theuser in quickly finding regions of interest.

A number of methods of using the computer system to automatically divideelectrograms into these categories are possible. All of these methodsshare the overall goal of segmenting the electrogram into regions withand without activation and then categorizing based on those segments.One method includes:

(a) For each electrogram, the computer system marks each sample of theelectrogram that exceeds the activation threshold for the annotationcriteria. The act of marking indicates that the sample of theelectrogram is considered to during a period of activation.

(b) For each marked sample of each electrogram, the computer systemmarks adjacent samples within a specified window (maximum sameactivation duration). This fills in small gaps that arise, for example,as the activation detection signal transitions from positive tonegative.

(c) For each electrogram, the computer system finds all the sequences ofcontiguous unmarked samples that are longer than a second specifiedwindow (minimum distinct activation separation). These are the periodsof no activation. Discard any period that begins at the start of theelectrogram or ends at the end of the electrogram as these periods arenot between activations.

(d) For each electrogram, the computer system finds all the sequences ofcontiguous marked samples that are longer than a third specified window(minimum fractionation duration).

(e) The computer system categorizes the electrogram according to thefollowing rules:

If samples are marked, the electrogram has no activation.

If a contiguous marked sequence longer than the minimum fractionationduration exists, the electrogram is fractionated.

If one contiguous unmarked sequence longer than the minimum distinctactivation separation exists between marked samples, the electrogram hasa double deflection.

If more than one contiguous unmarked sequence longer than the minimumdistinct activation separation exists between marked samples, theelectrogram has multiple deflections.

Otherwise, the electrogram is a normal activation.

FIG. 12 depicts this process for an electrogram with three deflections.“U” is the unipolar and “B” is the bipolar electrogram. The first andsecond step of the method above mark the electrogram samples within thetall boxes because some of those samples exceed the activationthreshold. The third step segments out the three short boxes. The lastof these is discarded because it is at the end of the cycle. Since twosufficiently long period of no activation exist, the method annotatesthis electrogram as a multiple deflection.

Because this annotation is intended to focus attention on specific sitesfor manual inspection, false positives for double deflections, multipledeflections, and fractionation should be minimized. One method ofreducing the false positive rate is to use a higher activation threshold(the multiple activation threshold) when marking samples for theseannotations. This threshold may be determined by scaling the activationthreshold by an adjustable ratio. Furthermore, the annotation criteriafor no activation, fractionation, and counting deflections need not bethe same.

User Input Propagation

Automatic methods may not always annotate electrograms in the waydesired by users. Therefore, user input in the form of manual annotationmay be provided in addition to the annotations automatically generatedby the computer system. A manual annotation is also designated manualoverride, because it locally overrides the computers automated decision.Because neighboring electrograms are likely to be similarly annotated,propagation of manual overrides to neighboring electrograms by thecomputer system can dramatically reduce the number of electrograms thatmust be manually annotated.

Two types of manual overrides that can be automatically propagated bythe computer system include categorical annotation overrides such astagging as no activation and value annotation overrides such as changingactivation time.

Categorical Annotation Overrides

For this type of override, a categorical annotation such as noactivation, double deflection, or fractionation is changed from theoriginal automatically generated annotation by the user for a particularelectrogram. The override may specify normal activation to remove anautomatic categorization such as double deflection that was incorrectlydetermined. The computer system applies the same categorical annotationsto neighboring electrograms (e.g., spatially correlated electrograms)with similar characteristics.

Propagating categorical overrides includes three functions:

(a) Electrogram clustering,

(b) Electrogram annotation method change and

(c) Conflict resolution rule.

Electrogram Clustering

Electrogram clustering for categorical overrides can use the samemethods as described above for the spatial consistency method. Sinceeach categorical override is manually determined, a separate radius ofinfluence could be specified for each override. For example, a separatedistance to the specified measured signal could be specified for eachoverride.

Electrogram Annotation Method Change

To bias the automatic annotation system towards the manual override nearthe overridden electrogram, some aspect of the automatic annotationsystem must be changed for the neighboring electrograms. Two basicapproaches exist for this alteration: locally altering annotationcriteria such as the activation threshold and locally altering theconfidences used by the spatial consistency method.

For the approach of changing annotation criteria, for each type ofcategorical override the computer system adjusts annotation criteria inan appropriate way for electrograms within the associated cluster. Thisdegree of the adjustment may be adjustable and the strength of thisadjustment can be a function of distance from the overriddenelectrogram. One possible set of modification methods is as follows:

For a no activation override, the computer system increases theactivation threshold (and, if implemented, the multiple activationthreshold) for neighboring electrograms by an adjustable percentage thatdeclines as a function of distance between the overridden location andthe neighbors. The computer system then re-determines the annotationsfor the neighboring electrograms based on the adjusted activationthreshold.

For a double deflection, multiple deflection, or fractionation override,the computer system decreases the activation threshold (and, ifimplemented, the multiple activation threshold) for neighboringelectrograms by an adjustable percentage that declines as a function ofdistance. The computer system then re-determines the annotations for theneighboring electrograms based on the adjusted activation threshold.

For a normal activation override, the computer system decreases theactivation threshold (and, if implemented, increase the multipleactivation threshold) for neighboring electrograms by an adjustablepercentage that declines as a function of distance. The computer systemthen re-determines the annotations for the neighboring electrogramsbased on the adjusted activation threshold.

Numerous functions may be used to change the adjustment amount as afunction of distance. One such function is one plus cosine of times theratio of the distance between the electrograms and the maximum distanceincluded in the cluster.

For all of these adjustments, an alternative to applying a fixedadjustment is to find the appropriate activation threshold to qualifythe overridden electrogram for the selected category, and then apply adistance attenuated version of that threshold to the neighboringelectrograms.

The approach of changing confidences works similarly to the approach ofchanging annotation criteria except the confidences are adjusted asfollows:

For electrograms near a no activation override, if the confidence isabove an adjustable threshold, the confidence is not changed by thecomputer system and the electrogram is considered as activated and istimed normally. If the confidence is below the threshold, the confidenceis set to zero by the computer system and the electrogram is consideredas not activated.

For electrograms near an activation override that were determined to beactivating by the automatic system, the electrogram is treated normallyexcept the timing annotation may be adjusted by the methods describedbelow for value annotation overrides.

For electrograms near an activation override that were determined to notbe activating by the automatic system, the electrogram is considered tohave small, uniform confidence. This forces the electrogram to be timedby the computer system in a way that is consistent with the neighboringelectrograms that have activation. If spatial consistency is notenabled, the electrogram is annotated at the largest annotationcandidate, even if that sample does not exceed threshold.

Conflict Resolution Rule

Conflict resolution is necessary when an electrogram is within theneighborhoods of two or more possibly inconsistent categoricaloverrides. The conflict resolution rule determines how the computersystem combines the multiple overrides to effect electrograms withintheir radii of influence and/or their set distances.

One method is to simply have the computer system consider the closestoverride and ignore the rest when annotating each electrogram.

A second method is to have the computer system combine the effects ofthe overrides on either the activation thresholds or the confidencesaccording to a function such as inverse distance weighting.

Value Annotation Overrides

For this type of override, an annotation with a range of possible valuessuch as activation time is manually set for a particular electrogram.Neighboring electrograms with similar characteristics should beinfluenced by the overridden annotation (e.g., the computer systemdetermines the annotation for the neighboring electrograms based in parton the override). For example, a region may have numerous electrogramswith similar double deflections. When the user manually moves theactivation time from one deflection to another, the computer systemshould modify the surrounding electrograms to follow suit.

This problem is closely related to the spatial consistency. One way ofsolving this problem is by extending the spatial consistency method torespect manual annotations. This extension can be made by preceding thefirst step of the annotation adjustment method given for spatialconsistency with the following steps:

For each overridden electrogram, the computer system sets the confidenceto the highest possible level at the overridden sample and sets allother confidence values to no confidence. This prevents the overriddenannotation from changing.

For neighboring electrograms that are not activating according to theautomatic criteria, the computer system sets the confidence to a small,uniform value and initialize the annotation with the override value.This ensures the electrogram is timed consistently with surroundingpoints because it can change freely away from the initial value.

For neighboring electrograms that are activating according to theautomatic criteria, the computer system uses a biased confidence for allcomputations and optionally initializes the annotation at the highestconfidence sample within a given window around the override value. Thisstarts the annotation close to the override and increases the likelihoodthat the annotation will stay close to the override but still allows theannotation to move back to the unbiased value if that activation issufficiently strong.

The biased confidences are the automatic confidences adjusted by thecomputer system to account for the neighboring manual overrides. Anumber of functions could be used to compute the biased confidence. Onesuch function is adding to the baseline confidence a value that startswith an adjustable base strength and attenuates that strength as afunction of distance from the override and time difference between thesample associated with the confidence and the override value. Theattenuation functions could be of many forms; one form is a raisedcosine of the ratio of the distance or time difference to the maximumdistance or time difference.

An example of this process is shown FIG. 13. “U1” and “U2” areneighboring unipolar electrograms. Manual annotation of U2 from theautomatically selected early time to the later time (step #1) willincrease the confidence associated with the smaller minima in the d(U1)trace (step #2), thereby adjusting the timing of U1 to the later time(step #3).

Surface Map Interpolation

Once surface data has been annotated, the computer system displays thesurface data to the operator. For example, the annotated data may bepresented in color or using any of a number of textures on surfacegeometry. In the case of using an inverse Laplace operator to generatemap surface data, the resultant dataset can have values on every pointon the surface geometry and no further surface interpolation isnecessary.

In the case of using finding points on the chamber, a surfaceinterpolation scheme may be necessary. For example, surfaceinterpolation may take all annotation values in the surface map data andprovide an interpolate value for them on each of the vertices used torepresent the surface. The surface interpolation can follow any of anumber of schemes including 3D Kriging, or Mean Value Interpolationexplained in Tao Ju, Scott Schaefer, and Joe Warren. 2005. Mean valuecoordinates for closed triangular meshes. ACM Trans. Graph. 24, 3 (July2005), 561-566.

Representative System

FIG. 14 shows a schematic diagram of an exemplary embodiment of anon-contact system 200. The non-contact system 200 includes a moveablecatheter 210 having multiple spatially distributed electrodes. Duringthe signal acquisition stage of the non-contact mapping procedure thecatheter 210 is displaced to multiple locations within the heart chamberinto which catheter 210 is inserted.

In some embodiments the distal end of the catheter 210 is fitted withmultiple electrodes spread somewhat uniformly over the catheter. Forexample, the electrodes may be mounted on the catheter 210 following a3D olive shape. The electrodes are mounted on a device capable ofdeploying the electrodes into the desired shape while inside the heart,and retracting the electrodes when the catheter is removed from theheart. To allow deployment into a 3D shape in the heart, electrodes maybe mounted on a balloon, or shape memory material such as Nitinol.

At each of the locations to which the catheter 210 is moved, thecatheter's multiple electrodes acquire signals resulting from theelectrical activity in the heart cavity. Consequently, reconstructingand presenting to a user (such as a doctor and/or technician)physiological data pertaining to the heart's electrical activity may bebased on information acquired at multiple locations, thereby providing amore accurate and faithful reconstruction of physiological behavior ofthe endocardium surface. The acquisition of signals at multiple catheterlocations in the heart chamber enables the catheter to effectively actas a “mega-catheter” whose effective number of electrodes and electrodespan is proportional to the product of the number of locations in whichsignal acquisition is performed and the number of electrodes thecatheter has.

To enhance the quality of the reconstructed physiological information atthe endocardium surface, in some embodiments the catheter 210 is movedto more than three locations (for example, more than 5, 10, or even 50locations) within the heart chamber. Further, the spatial range overwhich the catheter is moved may be larger than one third (⅓) of thediameter of the heart cavity (for example, larger than 35%, 40%, 50% oreven 60% of the diameter of the heart cavity). Additionally, in someembodiments the reconstructed physiological information is computedbased on signals measured over several heart beats, either at a singlecatheter location within the heart chamber or over several locations. Incircumstances where the reconstructed physiological information is basedon multiple measurements over several heart beats, the measurements aresynchronized with one another so that the measurement are performed atapproximately the same phase of the heart cycle. The signal measurementsover multiple beats can be synchronized based on features detected fromphysiological data such as surface ECG or intracardiac electrograms.

Non-contact mapping system 200 further includes the processing unit 220which performs several of the operations pertaining to the non-contactmapping procedure, including the reconstruction procedure to determinethe physiological information at the endocardium surface (e.g., asdescribed above). To expedite the computational operations performed bythe non-contact mapping system 200, the processing unit 220 can compute,generally prior to the insertion of the catheter into the heart chamberand/or before signal acquisition by the catheter's electrodes hascommenced, transformation functions that can be used in real-time tofacilitate the reconstruction process. Once the catheter 210 is insertedand is displaced to a particular location in the heart chamber, themapping procedure can be performed expeditiously by computing inreal-time those transformation components that were not computed aheadof the signal acquisition stage, and combining those components with theappropriate pre-processed transformation components to obtain theoverall transformation function(s). That overall transformation functionis applied to the acquired raw data to perform the inversereconstruction operation.

The processing unit 220 also performs a catheter registration procedure.The location of the catheter 210 inserted into the heart chamber can bedetermined using a conventional sensing and tracking system (not shown)that provide the 3D spatial coordinates of the catheter and/or itsmultiple electrodes with respect to the catheter's coordinate system asestablished by the sensing and tracking system. However, to perform themapping procedure and reconstruct physiological information on theendocardium surface, it is necessary to align the coordinate system ofthe catheter 210 with the endocardium surface's coordinate system. Theprocessing unit 220 (or some other processing module of system 200)determines a coordinate system transformation function that transformsthe 3D spatial coordinates of the catheter's locations into coordinatesexpressed in terms of the endocardium surface's coordinate system, orvice-versa.

The processing unit 220 also performs post-processing operations on thereconstructed physiological information to extract and display usefulfeatures of the information to the operator of the system 200 and/orother persons (e.g., a physician).

As further shown in FIG. 8, the signals acquired by the multipleelectrodes of catheter 210 are passed to the processing unit 220 via thesignal conditioning module 240. The signal conditioning module 240receives the signals communicated from the catheter 210 and performssignal enhancement operations on the signals before they are forwardedto the processing unit 220. Signal conditioning hardware is used toamplify, filter and continuously sample intracardiac potential measuredby each electrode. The intracardiac signals typically have a maximumamplitude of 60 mV, with a mean of a few millivolts. In some embodimentsthe signals are bandpass filtered in a frequency range (e.g., 0.5-500Hz) and sampled with analog to digital converters (e.g., with 15-bitresolution at 1 kHz). To avoid interference with electrical equipment inthe room, the signal can be filtered to remove the frequencycorresponding to the power supply (e.g., 60 Hz). Other types of signalprocessing operations such as spectral equalization, automatic gaincontrol, etc. may also take place. The resultant processed signals areforwarded by the module 240 to the processing unit 220 for furtherprocessing.

As further shown in FIG. 14, the non-contact mapping system 200 alsoincludes peripheral devices such as printer 250 and/or display device270, both of which are interconnected to the processing unit 220.Additionally, the mapping system 200 includes storage device 260 that isused to store data acquired by the various interconnected modules,including the volumetric images, raw data measured by electrodes and theresultant endocardium representation computed there from, the partiallycomputed transformations used to expedite the mapping procedures, thereconstructed physiological information corresponding to the endocardiumsurface, etc.

Other Embodiments

The methods and systems described herein are not limited to a particularhardware or software configuration, and may find applicability in manycomputing or processing environments. The methods and systems can beimplemented in hardware, or a combination of hardware and software,and/or can be implemented from commercially available modulesapplications and devices. Where the implementation of the systems andmethods described herein is at least partly based on use ofmicroprocessors, the methods and systems can be implemented in one ormore computer programs, where a computer program can be understood toinclude one or more processor executable instructions. The computerprogram(s) can execute on one or more programmable processors, and canbe stored on one or more storage medium readable by the processor(including volatile and non-volatile memory and/or storage elements),one or more input devices, and/or one or more output devices. Theprocessor thus can access one or more input devices to obtain inputdata, and can access one or more output devices to communicate outputdata. The input and/or output devices can include one or more of thefollowing: Random Access Memory (RAM), Redundant Array of IndependentDisks (RAID), floppy drive, CD, DVD, magnetic disk, internal hard drive,external hard drive, memory stick, or other storage device capable ofbeing accessed by a processor as provided herein, where suchaforementioned examples are not exhaustive, and are for illustration andnot limitation.

The computer program(s) can be implemented using one or more high levelprocedural or object-oriented programming languages to communicate witha computer system; however, the program(s) can be implemented inassembly or machine language, if desired. The language can be compiledor interpreted. The device(s) or computer systems that integrate withthe processor(s) can include, for example, a personal computer(s),workstation (e.g., Sun, HP), personal digital assistant (PDA), handhelddevice such as cellular telephone, laptop, handheld, or another devicecapable of being integrated with a processor(s) that can operate asprovided herein. Accordingly, the devices provided herein are notexhaustive and are provided for illustration and not limitation.

References to “a microprocessor” and “a processor”, or “themicroprocessor” and “the processor,” can be understood to include one ormore microprocessors that can communicate in a stand-alone and/or adistributed environment(s), and can thus be configured to communicatevia wired or wireless communications with other processors, where suchone or more processor can be configured to operate on one or moreprocessor-controlled devices that can be similar or different devices.Furthermore, references to memory, unless otherwise specified, caninclude one or more processor-readable and accessible memory elementsand/or components that can be internal to the processor-controlleddevice, external to the processor-controlled device, and can be accessedvia a wired or wireless network using a variety of communicationsprotocols, and unless otherwise specified, can be arranged to include acombination of external and internal memory devices, where such memorycan be contiguous and/or partitioned based on the application.Accordingly, references to a database can be understood to include oneor more memory associations, where such references can includecommercially available database products (e.g., SQL, Informix, Oracle)and also proprietary databases, and may also include other structuresfor associating memory such as links, queues, graphs, trees, with suchstructures provided for illustration and not limitation.

Accordingly, other embodiments are within the scope of the followingclaims.

We claim:
 1. A method for providing information about a patient's heart,the method comprising: measuring signals from one or more electrodes atmultiple positions in the heart cavity in response to electricalactivity in the patient's heart cavity over multiple heart beat cycles;generating, by a computer, annotation information for the measuredsignals by applying one or more operators to the measured signals;receiving, from an operator, a change to the annotation information fora specified measured signal; modifying, by the computer, annotationinformation for one or more neighboring additional measured signalsbased on the change to generate modified annotation information; andgenerating, by the computer, an electroanatomical representation of thepatient's heart that includes at least some of the annotationinformation and at least some of the modified annotation information. 2.The method of claim 1, wherein generating the annotation informationcomprises: identifying a time annotation for at least one electrogram.3. The method of claim 1, wherein: receiving, from the operator, thechange to the annotation information includes receiving, from theoperator, a change to an activation time for a specified measuredsignal; and wherein modifying the annotation information includesautomatically adjusting activation times for one or more neighboringadditional measured signals based on the operator change.
 4. The methodof claim 1, wherein generating the annotation information comprises:identifying at least one region of the heart having fractionation. 5.The method of claim 1, wherein generating the annotation informationcomprises: identifying at least one of a region of the heart havingdouble deflections, a region of the heart having multiple deflections, aregion of the heart having fractionation, a region of the heart havingdouble activation, and a region of the heart having no activation. 6.The method of claim 1, wherein generating the annotation informationcomprises: identifying at least one of a region of the heart havingearly deflections, a region of the heart having late deflections, and aregion of the heart having far field effects with no activation.
 7. Themethod of claim 1, wherein modifying the annotation information for oneor more additional measured signals comprises automatically adjustingannotation information for one or more neighboring additional measuredsignals in at least one of spatial proximity and temporal proximity tothe specified measured signal.
 8. A method for providing informationabout a patient's heart, the method comprising: measuring signals fromone or more electrodes at multiple positions in the heart cavity inresponse to electrical activity in the patient's heart cavity overmultiple heart beat cycles; generating, by a computer, annotationinformation for the measured signals by applying one or more operatorsto the measured signals to identify at least one of a region of theheart having early deflections and a region of the heart having latedeflections; receiving, from an operator, a change to the annotationinformation for a specified measured signal; modifying, by the computer,annotation information for one or more neighboring additional measuredsignals based on the change to generate modified annotation information;and generating, by the computer, an electroanatomical representation ofthe patient's heart that includes at least some of the annotationinformation.
 9. The method of claim 8, wherein: generating theannotation information comprises applying an algorithm to the measuredsignals to detect an early deflection; and generating theelectroanatomical representation comprises identifying a region of earlydeflections.
 10. The method of claim 8, wherein: generating theannotation information comprises applying an algorithm to the measuredsignals to detect a late deflection; and generating theelectroanatomical representation comprises identifying a region of latedeflections.
 11. The method of claim 8, wherein modifying the annotationinformation for one or more neighboring additional measured signalscomprises automatically adjusting annotation information for one or moreneighboring additional measured signals in spatial proximity to thespecified measured signal.
 12. The method of claim 8, wherein generatingthe annotation information comprises generating annotation informationbased on a specified electrogram and spatially or temporally neighboringelectrograms.
 13. The method of claim 8, wherein generating theannotation information comprises generating annotation information for aspecific location of the endocardium based on the signals measured at anelectrode location corresponding to the specific location and signalsmeasured at one or more additional electrodes at locations in spatialproximity to the electrode location corresponding to the specificlocation.
 14. The method of claim 8, wherein generating the annotationinformation comprises generating annotation information for a specificlocation of the endocardium based on the signals measured at anelectrode location corresponding to the specific beat and signalsmeasured at one or more previous beats at the same electrode.
 15. Asystem for providing information about a patient's heart, the systemcomprising: one or more electrodes for measuring signals at multiplepositions in the heart cavity in response to electrical activity in thepatient's heart cavity over multiple heart beat cycles; and anelectronic processor coupled to the one or more electrodes, wherein theelectronic processor is configured to: generate annotation informationfor the measured signals by applying one or more operators to themeasured signals to identify a time annotation for at least oneelectrogram; receive, from an operator, a change to the automaticallygenerated annotation information for a specified measured signal; modifyannotation information for one or more neighboring additional measuredsignals based on the change to generate modified annotation information;and generate an electroanatomical representation of the patient's heartthat includes at least some of the annotation information.
 16. Thesystem of claim 15, wherein the electronic processor is configured to:receive, from the operator, a change to an activation time for thespecified measured signal; and automatically adjust activation times forone or more neighboring additional measured signals based on theoperator change to the activation time.
 17. The system of claim 15,wherein the electronic processor is configured to generate theannotation information to identify at least one of a region of the hearthaving double deflections, a region of the heart having multipledeflections, a region of the heart having fractionation, a region of theheart having double activation, a region of the heart having noactivation, a region of the heart having early deflections, a region ofthe heart having late deflections, and a region of the heart having farfield effects with no activation.
 18. The system of claim 15, whereinthe electronic processor is configured to adjust the annotationinformation for one or more neighboring additional measured signals byautomatically adjusting annotation information for one or moreneighboring additional measured signals in spatial proximity to thespecified measured signal.
 19. The system of claim 15, wherein theelectronic processor is configured to generate the annotationinformation by generating annotation information for a specific locationof the endocardium based on the signals measured at an electrodelocation corresponding to the specific location and signals measured atone or more additional electrodes at locations in spatial proximity tothe electrode location corresponding to the specific location.
 20. Thesystem of claim 15, wherein the electronic processor is configured togenerate the annotation information by generating annotation informationfor a specific location of the endocardium based on the signals measuredat an electrode location corresponding to the specific beat and signalsmeasured at one or more previous beats at the same electrode.